From 09a22231624b15d4882883a3c8490b44e195ab67 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jorge=20Rodr=C3=ADguez=20Ara=C3=BAjo?=
 <jorge@abraiasoftware.com>
Date: Sat, 31 Aug 2024 18:09:49 +0200
Subject: [PATCH] Update yolo notebook with initial dashboard

---
 notebooks/YOLOv8.ipynb | 6102 +++++++++++++++++++++++++++++++++-------
 1 file changed, 5163 insertions(+), 939 deletions(-)

diff --git a/notebooks/YOLOv8.ipynb b/notebooks/YOLOv8.ipynb
index 2c950a3..4cc9293 100644
--- a/notebooks/YOLOv8.ipynb
+++ b/notebooks/YOLOv8.ipynb
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "OTztNYjoEvTr"
@@ -29,88 +29,86 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 113,
+          "height": 645,
           "referenced_widgets": [
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-            "5a104c6b98854309b678f4487d9a1a59",
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         "id": "LX2njgUxUxB-",
-        "outputId": "53f84024-e1f0-4b6a-8e27-b86af75cf43b"
+        "outputId": "6e49b8a1-9a3d-40fc-c52f-f96e0649c011"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c2a636831b27463ea830f18ca124a903",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0/140 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8466ad45862641bc89b1c3e8eba4336f",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "  0%|          | 0/30 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b83a8387f50c4d1bace59771b544bd45",
+              "model_id": "5f5088bd815e4524b036ceead39c657d",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "  0%|          | 0/30 [00:00<?, ?it/s]"
+              "VBox(children=(Dropdown(description='Project', options=('camera', 'circles', 'edge', 'final-demo', 'hymenopter…"
             ]
           },
           "metadata": {},
@@ -118,624 +116,252 @@
         }
       ],
       "source": [
+        "from logging.config import valid_ident\n",
+        "#@markdown ### Dataset\n",
+        "\n",
         "import json\n",
+        "import glob\n",
+        "import shutil\n",
         "import itertools\n",
         "from PIL import Image\n",
         "from tqdm.contrib.concurrent import process_map\n",
         "from sklearn.model_selection import train_test_split\n",
         "\n",
         "\n",
+        "def load_projects():\n",
+        "    folders = multiple.list_files()[1]\n",
+        "    return [folder['name'] for folder in folders if folder['name'] not in ('export', '.export')]\n",
+        "\n",
+        "\n",
+        "def load_annotations(dataset):\n",
+        "    annotations = json.loads(multiple.load_file(f\"{dataset}/annotations.json\"))\n",
+        "    for annotation in annotations:\n",
+        "        annotation['path'] = f\"{dataset}/{annotation['filename']}\"\n",
+        "    return annotations\n",
+        "\n",
+        "\n",
+        "def load_labels(annotations):\n",
+        "    labels = []\n",
+        "    for annotation in annotations:\n",
+        "        for object in annotation.get('objects', []):\n",
+        "            label = object.get('label')\n",
+        "            if label and label not in labels:\n",
+        "                labels.append(label)\n",
+        "    return list(set(labels))\n",
+        "\n",
+        "\n",
+        "def load_task(annotations):\n",
+        "    label, box, polygon = False, False, False\n",
+        "    for annotation in annotations:\n",
+        "        for object in annotation.get('objects', []):\n",
+        "            if 'polygon' in object:\n",
+        "                polygon = True\n",
+        "            elif 'box' in object:\n",
+        "                box = True\n",
+        "            elif 'label' in object:\n",
+        "                label = True\n",
+        "    if polygon:\n",
+        "        return 'segment'\n",
+        "    if box:\n",
+        "        return 'detect'\n",
+        "    if label:\n",
+        "        return 'classify'\n",
+        "\n",
+        "\n",
         "def download_file(path, folder):\n",
-        "    dest = os.path.join(folder, 'images', os.path.basename(path))\n",
+        "    dest = os.path.join(folder, os.path.basename(path))\n",
         "    if not os.path.exists(dest):\n",
         "        multiple.download_file(path, dest)\n",
         "    return dest\n",
         "\n",
         "\n",
-        "def save_annotation(path, annotation, folder):\n",
-        "    im = Image.open(os.path.join(folder, 'images', os.path.basename(path)))\n",
-        "    cords, bbox = annotation['polygon'], annotation['box']\n",
-        "    # Convert polygon or box to yolo format\n",
-        "    if task == 'segmentation':\n",
-        "        label_line = '0 ' + ' '.join([f\"{cord[0] / im.width} {cord[1] / im.height}\" for cord in cords])\n",
+        "def save_annotation(annotation, folder, classes, task):\n",
+        "    if task == 'classify':\n",
+        "        for object in annotation.get('objects', []):\n",
+        "            label = object.get('label')\n",
+        "            if label:\n",
+        "                src = os.path.join(folder, annotation['filename'])\n",
+        "                dest = os.path.join(folder, label, annotation['filename'])\n",
+        "                shutil.move(src, dest)\n",
         "    else:\n",
-        "        label_line = f\"0 {(bbox[0] + bbox[2] / 2) / im.width} {(bbox[1] + bbox[3] / 2) / im.height} {bbox[2] / im.width} {bbox[3] / im.height}\"\n",
-        "    label_path = os.path.join(folder, 'labels',  f\"{os.path.splitext(os.path.basename(path))[0]}.txt\")\n",
-        "    with open(label_path, 'w') as f:\n",
-        "        f.write(label_line)\n",
+        "        im = Image.open(os.path.join(folder, 'images', annotation['filename']))\n",
+        "        label_lines = []\n",
+        "        for object in annotation.get('objects', []):\n",
+        "            label, bbox, cords = object.get('label'), object.get('box'), object.get('polygon')\n",
+        "            # Convert polygon or box to yolo format\n",
+        "            label_line = ''\n",
+        "            if task == 'segment' and cords:\n",
+        "                label_line = f\"{classes.index(label)} \" + ' '.join([f\"{cord[0] / im.width} {cord[1] / im.height}\" for cord in cords])\n",
+        "            elif task == 'detect' and bbox:\n",
+        "                label_line = f\"{classes.index(label)} {(bbox[0] + bbox[2] / 2) / im.width} {(bbox[1] + bbox[3] / 2) / im.height} {bbox[2] / im.width} {bbox[3] / im.height}\"\n",
+        "            elif task == 'classify':\n",
+        "                label_line = f\"{classes.index(label)}\"\n",
+        "            label_lines.append(label_line)\n",
+        "        label_path = os.path.join(folder, 'labels',  f\"{os.path.splitext(annotation['filename'])[0]}.txt\")\n",
+        "        with open(label_path, 'w') as f:\n",
+        "            f.write('\\n'.join(label_lines))\n",
         "\n",
         "\n",
-        "def save_data(paths, annotations, folder):\n",
-        "    os.makedirs(os.path.join(folder, 'images'), exist_ok=True)\n",
-        "    process_map(download_file, paths, itertools.repeat(folder), max_workers=5)\n",
-        "    os.makedirs(os.path.join(folder, 'labels'), exist_ok=True)\n",
-        "    for path, annotation in zip(paths, annotations):\n",
-        "        label_line = save_annotation(path, annotation, folder)\n",
+        "def save_data(annotations, folder, classes, task):\n",
+        "    if (task == 'classify'):\n",
+        "        os.makedirs(os.path.join(folder), exist_ok=True)\n",
+        "        paths = [annotation['path'] for annotation in annotations]\n",
+        "        process_map(download_file, paths, itertools.repeat(folder), max_workers=5)\n",
+        "        for label in classes:\n",
+        "            os.makedirs(os.path.join(folder, label), exist_ok=True)\n",
+        "        for annotation in annotations:\n",
+        "            save_annotation(annotation, folder, classes, task)\n",
+        "    else:\n",
+        "        os.makedirs(os.path.join(folder, 'images'), exist_ok=True)\n",
+        "        paths = [annotation['path'] for annotation in annotations]\n",
+        "        process_map(download_file, paths, itertools.repeat(os.path.join(folder, 'images')), max_workers=5)\n",
+        "        os.makedirs(os.path.join(folder, 'labels'), exist_ok=True)\n",
+        "        for annotation in annotations:\n",
+        "            save_annotation(annotation, folder, classes, task)\n",
         "\n",
         "\n",
-        "def save_config(path, class_names):\n",
+        "def save_config(dataset, classes):\n",
         "    yaml_content = f'''\n",
         "    train: {os.path.join(os.getcwd(), dataset, 'train/images')}\n",
         "    val: {os.path.join(os.getcwd(), dataset, 'val/images')}\n",
         "    test: {os.path.join(os.getcwd(), dataset, 'test/images')}\n",
-        "    names: {class_names}\n",
+        "    names: {classes}\n",
         "    '''\n",
+        "    path = os.path.join(dataset, 'data.yaml')\n",
         "    with open(path, 'w') as f:\n",
         "        f.write(yaml_content)\n",
         "\n",
         "\n",
-        "def create_dataset(dataset):\n",
-        "    annotations = json.loads(multiple.load_file(f\"{dataset}/annotations.json\"))\n",
-        "    paths = [f\"{dataset}/{annotation['filename']}\" for annotation in annotations]\n",
-        "    class_names = list(set([annotation['label'] for annotation in annotations]))\n",
-        "    train_paths, test_paths, train_annotations, test_annotations = train_test_split(paths, annotations, test_size=0.3)\n",
-        "    val_paths, test_paths, val_annotations, test_annotations = train_test_split(test_paths, test_annotations, test_size=0.5)\n",
-        "    data_paths = {'train': train_paths, 'val': val_paths, 'test': test_paths}\n",
-        "    data_annotations = {'train': train_annotations, 'val': val_annotations, 'test': test_annotations}\n",
-        "    for x in ['train', 'val', 'test']:\n",
-        "        save_data(data_paths[x], data_annotations[x], f\"{dataset}/{x}\")\n",
-        "    save_config(os.path.join(dataset, 'data.yaml'), class_names)\n",
-        "    return class_names\n",
+        "def split_dataset(annotations):\n",
+        "    train, test = train_test_split(annotations, test_size=0.3)\n",
+        "    val, test = train_test_split(test, test_size=0.5)\n",
+        "    return train, val, test\n",
         "\n",
         "\n",
-        "task = 'detection'\n",
-        "dataset = 'circles'\n",
+        "def create_dataset(dataset, task, classes):\n",
+        "    annotations = load_annotations(dataset)\n",
+        "    train, val, test = split_dataset(annotations)\n",
+        "    data_annotations = {'train': train, 'val': val, 'test': test}\n",
+        "    for x in ['train', 'val', 'test']:\n",
+        "        save_data(data_annotations[x], f\"{dataset}/{x}\", classes, task)\n",
+        "    save_config(dataset, classes)\n",
+        "\n",
         "\n",
-        "classes = create_dataset(dataset)\n",
-        "print(classes)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IF_2w0IZFD1F",
-        "outputId": "bda11364-4f36-41fa-b40f-73ec30fa2c62"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 6.23M/6.23M [00:00<00:00, 55.5MB/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Ultralytics YOLOv8.1.27 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
-            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=circles/data.yaml, epochs=7, time=None, patience=100, batch=8, imgsz=120, save=True, save_period=-1, cache=False, device=cpu, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
-            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 755k/755k [00:00<00:00, 21.8MB/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Overriding model.yaml nc=80 with nc=1\n",
-            "\n",
-            "                   from  n    params  module                                       arguments                     \n",
-            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
-            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
-            "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
-            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
-            "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
-            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
-            "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
-            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
-            "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
-            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
-            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
-            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
-            " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
-            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
-            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
-            " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
-            " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
-            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
-            " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
-            " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
-            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
-            " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
-            " 22        [15, 18, 21]  1    751507  ultralytics.nn.modules.head.Detect           [1, [64, 128, 256]]           \n",
-            "Model summary: 225 layers, 3011043 parameters, 3011027 gradients, 8.2 GFLOPs\n",
-            "\n",
-            "Transferred 319/355 items from pretrained weights\n",
-            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
-            "Freezing layer 'model.22.dfl.conv.weight'\n",
-            "WARNING ⚠️ imgsz=[120] must be multiple of max stride 32, updating to [128]\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/circles/train/labels... 183 images, 0 backgrounds, 0 corrupt: 100%|██████████| 183/183 [00:00<00:00, 2038.92it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/circles/train/labels.cache\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/circles/val/labels... 57 images, 0 backgrounds, 0 corrupt: 100%|██████████| 57/57 [00:00<00:00, 2369.59it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/circles/val/labels.cache\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Plotting labels to runs/detect/train/labels.jpg... \n",
-            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
-            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
-            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
-            "Image sizes 128 train, 128 val\n",
-            "Using 0 dataloader workers\n",
-            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
-            "Starting training for 7 epochs...\n",
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        1/7         0G     0.8369      2.413     0.9201         13        128: 100%|██████████| 23/23 [00:09<00:00,  2.49it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  3.75it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57     0.0232          1       0.93      0.726\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        2/7         0G     0.6735     0.8102     0.8708         11        128: 100%|██████████| 23/23 [00:07<00:00,  2.88it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  3.94it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57          1      0.984      0.995      0.878\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        3/7         0G     0.6573     0.6757     0.8933          9        128: 100%|██████████| 23/23 [00:07<00:00,  3.09it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  2.85it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.993          1      0.995      0.873\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        4/7         0G     0.6058     0.6284     0.8633         15        128: 100%|██████████| 23/23 [00:06<00:00,  3.32it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  3.87it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.999          1      0.995      0.862\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        5/7         0G     0.6083     0.6188     0.8991         15        128: 100%|██████████| 23/23 [00:07<00:00,  2.95it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  3.96it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.999          1      0.995      0.926\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        6/7         0G     0.5428     0.6023     0.8639          7        128: 100%|██████████| 23/23 [00:07<00:00,  2.99it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  3.95it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.999          1      0.995      0.915\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "        7/7         0G     0.5338     0.6339     0.8653         16        128: 100%|██████████| 23/23 [00:07<00:00,  3.25it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:01<00:00,  2.41it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.999          1      0.995      0.918\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "7 epochs completed in 0.019 hours.\n",
-            "Optimizer stripped from runs/detect/train/weights/last.pt, 6.2MB\n",
-            "Optimizer stripped from runs/detect/train/weights/best.pt, 6.2MB\n",
-            "\n",
-            "Validating runs/detect/train/weights/best.pt...\n",
-            "Ultralytics YOLOv8.1.27 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
-            "Model summary (fused): 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:02<00:00,  1.87it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "                   all         57         57      0.999          1      0.995      0.926\n",
-            "Speed: 0.8ms preprocess, 27.2ms inference, 0.0ms loss, 0.3ms postprocess per image\n",
-            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
         "from ultralytics import YOLO\n",
+        "from PIL import ImageDraw\n",
         "\n",
-        "model_name = 'yolov8n-seg' if task == 'segmentation' else 'yolov8n'\n",
         "\n",
-        "model = YOLO(f\"{model_name}.pt\")\n",
+        "def build_model_name(model_name, task):\n",
+        "    if task == 'segment':\n",
+        "        model_name = f\"{model_name}-seg\"\n",
+        "    if task == 'classify':\n",
+        "        model_name = f\"{model_name}-cls\"\n",
+        "    return model_name\n",
         "\n",
-        "results = model.train(batch=8, device=\"cpu\", data=f\"{dataset}/data.yaml\", epochs=7, imgsz=120)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "75EODYjVDVzk",
-        "outputId": "9fddd226-8a89-4311-dd02-ebceeaf58819"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Ultralytics YOLOv8.1.27 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.20GHz)\n",
-            "Model summary (fused): 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n",
-            "\n",
-            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'runs/detect/train/weights/best.pt' with input shape (1, 3, 128, 128) BCHW and output shape(s) (1, 5, 336) (5.9 MB)\n",
-            "\n",
-            "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.15.0 opset 17...\n",
-            "\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 0.8s, saved as 'runs/detect/train/weights/best.onnx' (11.5 MB)\n",
-            "\n",
-            "Export complete (2.2s)\n",
-            "Results saved to \u001b[1m/content/runs/detect/train/weights\u001b[0m\n",
-            "Predict:         yolo predict task=detect model=runs/detect/train/weights/best.onnx imgsz=128  \n",
-            "Validate:        yolo val task=detect model=runs/detect/train/weights/best.onnx imgsz=128 data=circles/data.yaml  \n",
-            "Visualize:       https://netron.app\n"
-          ]
-        }
-      ],
-      "source": [
-        "model_src = model.export(format=\"onnx\")\n",
         "\n",
-        "multiple.upload_file(model_src, f\"{dataset}/{model_name}.onnx\")\n",
-        "multiple.save_json(f\"{dataset}/{model_name}.json\", {'task': task, 'inputShape': [1, 3, 128, 128], 'classes': classes})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 657
-        },
-        "id": "UcMsPpa-FK3S",
-        "outputId": "ee574a7b-3665-41da-8222-d53a36da7c6a"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/jpeg": 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KKKKAPcq0vD3/ACMulf8AX5D/AOhis2tLw9/yMulf9fkP/oYoA0fHn/I6ah/2z/8ARa1zldH48/5HTUP+2f8A6LWucoA8Noq3p+mXeq3DQWUPmyKu8ruC8ZAzyR6itL/hDdf/AOfD/wAjR/8AxVQ6kIuzZ0U8LXqx5qcG15JswqK3f+EN1/8A58P/ACNH/wDFUf8ACG6//wA+H/kaP/4ql7Wn/MvvL+oYv/n1L/wF/wCRhUVb1DTLvSrhYL2HypGXeF3BuMkZ4J9DVSrTTV0c04ShJxkrNBRRRTJCiiigAooooAKKKKACiiigD3KtLw9/yMulf9fkP/oYrNrS8Pf8jLpX/X5D/wChigDR8ef8jpqH/bP/ANFrXOV0fjz/AJHTUP8Atn/6LWucoA8NooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPcq0vD3/Iy6V/1+Q/+his2tLw9/wAjLpX/AF+Q/wDoYoA0fHn/ACOmof8AbP8A9FrXOV0fjz/kdNQ/7Z/+i1rnKAPDaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD3KtLw9/yMulf9fkP/AKGKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/0Wtc5XR+PP+R01D/tn/wCi1rnKAPDa7P4f+GbfxJJqCTWonMCo25pvLVAdwJLZAHbrXGV6H8MdU0/T7XXItTuPs9tciBPNBXKsCzDhiAeh4yPXtWVa/I7Hflii8VBSSa131Wz6eW503/CsdOLRxLpTeezSAq05UAIASc7/AEOewx0J7NX4Z6cYvNOlr5YJyVuiSQDgsAHyyg9xx71tR+MPDkFssEOoxbEW5UF54yT5kaqOh9Qc+g9ahbxP4ant7Z59TjFzbxCJUSaPY2GJDZLZHXpjnHUVwXn3f3n1ap4frCH/AICvPz9PQzrv4Y6batckabvhhd1D+ewZlVipYJvzjI69B615X4ls7fT/ABBdWtrH5cKbNq5JxlATyeepr3u58eaLqDyC3v4BcSCSGKNEt2Zw5bHznLDhsFRnPYjNcJqfwk8XeI9dvbuysPLjygxdboT9wDjcAG6dicd63otqerbVjysypwlhVyxhGXMtrLSz9PLY8por07/hQfjr/n1sv/Apar3vwP8AG1hatczWcLIuMiB2mfk44RFLH8BXXzI8D2Mu6+9f5nnNFbGoeHpdJvHs9Ru4bS5TBaGeCdHXIyMgx56EGqv2C2/6C9l/3xN/8bo5kHsZd196/wAyjRV77Bbf9Bey/wC+Jv8A43ST6esVo1zFe29wiyLGwjDggsGI+8o/umjmQnRla+n3r/MpUUUVRkFFFFAHuVaXh7/kZdK/6/If/QxWbWl4e/5GXSv+vyH/ANDFAGj48/5HTUP+2f8A6LWucro/Hn/I6ah/2z/9FrXOUAcR8J7aK58VXfnRmVYtPmlEQJHmFSpA45x3OOwNew2Fpa3iwebYyW8M17bq0IlfaylZMlcnoccHkjnmvn/wzqtzolxe6jZkC4gtgUJJ4JljHYg966bSfHHjLxFrUFhp0KXV/cSgoAZMlgOGJ38YGfmPQZrkq0pTndI+hwOOpYfDKE5NN6rfueniO0axgnj0jzmupJECRSSfutoGAvJ+bncc5+gran0vT5pLaS6MZe5EcZJEzOgEUf3AikFuc4brx9a0fDvw61C20+SPWtZYSSjDw6YWjjPJ6ls7uMdh361sL4CskWRU1bV1WRQrgXAG4AYAPy8jHFZrDzW6R2VM2w0neM5LfvfX79vxtqfK/wAQeNfg7/6Mv/oT1ydfY1z8JfCt5BtubaSWfPFzJsaQL/dGV246npnnrXkHxA+E+t+FbK41bSns9Q02L5pF/s+ESxLzliNmCo4yR7kgCuqlGUYKLR4WNq0K+InVjJ2b7f8ABPGKK2dL1GW51eyglhsmjknRHX7FCMgsAR92sarTd7M5JwioqUXe9+ltreb7hRRRVGQUUUUAFFFFABRRRQB7lWl4e/5GXSv+vyH/ANDFZtaXh7/kZdK/6/If/QxQBo+PP+R01D/tn/6LWucro/Hn/I6ah/2z/wDRa1zlAHhtFFFABRRRQAUUUUAFFFFABRXo+l/Dm2v9EtdSYaj5UsaM8qAeWGYA4zt9TVyT4W2sfnMP7TeOKQxtKq/KCDjugxzjg4PNc7xMEevHJMVJJq2vmeWUV6sfhRbIk5mOoQtCiyFJcKzAsFGAU9TWdrPw8tNJsLqWX+0IpooHlVJsLnAJHG0HHFH1mAPJMVZvTTz+Z51RRRXQeQFFFFAHuVaXh7/kZdK/6/If/QxWbWl4e/5GXSv+vyH/ANDFAGj48/5HTUP+2f8A6LWucro/Hn/I6ah/2z/9FrXOUAeG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHuVaXh7/kZdK/6/If/AEMVm1peHv8AkZdK/wCvyH/0MUAaPjz/AJHTUP8Atn/6LWucro/Hn/I6ah/2z/8ARa1zlAHhtXov+QDd/wDX1B/6DLVGr0X/ACAbv/r6g/8AQZamWxrR+J+j/JlGvRPh58JdW8bvFezE2ejCTbJOfvuO/lg9fTPQZ71L8IvhwPHGsS3V/vTSbIqZcKf3zf3A3T3PfBHrX1ha2sFlaxWtrEkMEShEjQYCgdhVGRzfhL4d+G/BkK/2XYIbrbte8lG+V+AD8x6A4zgYHtXVUUUAFFFFAGR4g8L6L4psvsmtafDdxjOwuPmTOMlWHKngcg189/EH4FXuhRzan4caS90+NDJLA5zNHz/DgfMAPx4719N0UAfAFXov+QDd/wDX1B/6DLXtPxu+F1tp0MvivRIjHEzj7ZaxoSqk/wDLQY6DPXtk+9eLRf8AIBu/+vqD/wBBlqZbGtH4n6P8mUaKKKoyCiiigD3KtLw9/wAjLpX/AF+Q/wDoYrNrS8Pf8jLpX/X5D/6GKANHx5/yOmof9s//AEWtc5XR+PP+R01D/tn/AOi1rnKAPH9KhkuIdQgiUvJJAiIo7kzRACvrL4ZfDqw8DaKsmzzNWuYwbmd1+Yd9g9AD+ZH0rwH4IWcd98SbaCUKU8ppCGXIOwqw4+q19c1K3ZrU+GHp+rCiiiqMgoIyMGiigD5w+KXw0tfDHibStf0aExafd30azW6jCQSFgRt9AecDtg9sCvD6+4/GNhHqPhTUIpMfu4/PUlQcNH84x6fdxn3r4cqV8TNpfwo+r/JBRRRVGIUUUUAFFFFABRRRQB7lWl4e/wCRl0r/AK/If/QxWbWl4e/5GXSv+vyH/wBDFAGj48/5HTUP+2f/AKLWucro/Hn/ACOmof8AbP8A9FrXOUAeG0UUUAFFFFABRRRQAUUUUAfQOi3dvL4V0OQai1u0GnRQtbqjZY9eCPl2nOTk568GtqbW7QalbTJKzRJqNxO21TkKxXawB78E/h2rzv4c/DgeOPEhur/emk2VvamXCn983kp8gbp7nvgj1r6c0vSbDRNPjsNMtY7W1j+7FGMAVyfV3LW+/l/wT6F5vCm/Zum3y3W69P5TyfStSstImYzaibxcKVCo+1T5qscbsHOASenTqaxPGNzBdeHJbS1e0bbDOcwiVVBZR1aVs9s9gPU5Ne/1n6zoemeIdPax1ayiu7ZiG8uQdx0I7ij6s7Wv+H/BJedwcnL2bu/7y7W/lPh46Rd7XZWtX2KzkJdxMcAZJwGyeATVGvSvHnw9bwF4taO3aSbTLq0uZLaRlPy/unyhPQsPbsQeM15rXSm72Z404w5FKPd/hbyXcKKKKoxPcq0vD3/Iy6V/1+Q/+his2tLw9/yMulf9fkP/AKGKANHx5/yOmof9s/8A0Wtc5XR+PP8AkdNQ/wC2f/ota5ygDw2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA9yrS8Pf8jLpX/X5D/6GKza0vD3/ACMulf8AX5D/AOhigDR8ef8AI6ah/wBs/wD0Wtc5XR+PP+R01D/tn/6LWucoA8NrW02zuNR0+SytImlubi+tooo16szLKAB+NZNeufADTDqPjGSYiMpYlbllcZz8kiDHuGdT+FTLY1o/E/R/kz6H8H+G4PCXhTT9FgIYW0eJHGfnkPLtgk4yxJx2zW5RRVGQUUUUAFFFFABRRRQBFc28d3ay20y7opUKOM4yCMGvjbxr4Yl8IahrOkyA+Wl5A8DY+9GVm2nqfp+FfZ1eDftI2ypYaRdBUDSzFCQOTtVjyf8AgX86mWxrR+J+j/JnzzRRRVGQUUUUAe5VpeHv+Rl0r/r8h/8AQxWbWl4e/wCRl0r/AK/If/QxQBo+PP8AkdNQ/wC2f/ota5yuj8ef8jpqH/bP/wBFrXOUAc38A/8AkqVt/wBes3/oNfWNfGnwu1yLw34xj1WcssMKASFSBhWkRD14xhufavsmORJoklidXjdQyspyGB6EGpW7Nanww9P1Y6iiiqMgooooAzvEH/It6p/15y/+gGvhGvsz4meIoNC8MCBpdtzqM6WkKKw3EMw3HB6jbwcf3hXxnUr4mbS/hR9X+SCiiiqMQooooAKKKKACiiigD3KtLw9/yMulf9fkP/oYrNrS8Pf8jLpX/X5D/wChigDR8ef8jpqH/bP/ANFrXOV0fjz/AJHTUP8Atn/6LWucoA8NooooAKKKKACiiigAooooA+zPhOgX4X6CR/Hbgn8yP6V2dcb8KCD8LvD4BBxbYPtya7Kph8KNcR/Fl6v8woooqjI4X4v2aXPw11WZmYNaxmVAO5KlMH8HP5Cvjqvsv4syxx/DDXVd1VpLcqgJwWOc4HqcAn8DXxpUr4mbS/hR9X+SCiiiqMT3KtLw9/yMulf9fkP/AKGKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/0Wtc5XR+PP+R01D/tn/wCi1rnKAPDaKKKACiiigAooooAKK6HTPB2oarp8V7BNarHJnAdmB4JHZT6Vdk+HWsxPskltEbAOGZwcEZB+76EGsnWpp2bO6GWYucVKNNtPVHI0V1yfDrWZSRHLaOQpYhWc4AGSfu9AKZ/wr7Vv+fiy/wC+3/8AiaXt6fcr+ysb/wA+2cpRRRWx54UUUUAFFFFAHuVaXh7/AJGXSv8Ar8h/9DFZtaXh7/kZdK/6/If/AEMUAaPjz/kdNQ/7Z/8Aota5yuj8ef8AI6ah/wBs/wD0Wtc5QB4bXt37Nf8AyMmt/wDXmn/odeI17V+zfPFF4o1ZJJERprZUjVjgu2S2B6nCsfoDUy2NaPxP0f5M+laKKKoyCiiigAooooAKKKKACvDv2lf+QDoX/X1J/wCgivca8N/aUZToehpkbhcuSO4BX/6xqZbGtH4n6P8AJnzlRRRVGQUUUUAe5VpeHv8AkZdK/wCvyH/0MVm1peHv+Rl0r/r8h/8AQxQBo+PP+R01D/tn/wCi1rnK6Px5/wAjpqH/AGz/APRa1zlAHjdh/wAeWqf9eq/+jo69y+D/AMYP+Pfw14luPSOzvZD+SOf5H8D7eFWN1FbC4SeF5Y54vLISQIR8ytnJB/u+nepPN0n/AJ8r3/wLT/43UapvQ6GozjH3krLz7vyPvCivknw18Y9a8MW5t7Zru6g/hjvblZAvJJwfLB5z610v/DSmtf8AQAsP+/j07vsT7OP86/H/ACPpGsrxF4i0zwto0+qarcCG3iH1Zz2VR3Jr5/m/aQ1yRNqaNZxHP3kkJP6givO9f8XSeJ7s3Orvqdy2chWvV2r1xhfLwOpou+wezj/Ovx/yNfxL4/1Lx744066uSYbKK6jFraBsrEu8cn1Y9z/TArgq1LW90y0u4bmOxuy8MiyKGu1wSDkZ/d1l0K972HU5VTUU76vv5d0goooqjAKKKKACiiigAooooA9yrS8Pf8jLpX/X5D/6GKza0vD3/Iy6V/1+Q/8AoYoA0fHn/I6ah/2z/wDRa1zldH48/wCR01D/ALZ/+i1rnKAPDaKKKACiiigAooooAKKKKAPoj4OfESK0uo/CWq3EUUP2eBrB2XHzNEpZC31ORn1PtXu9fCb39hM8UstpdiZI403RXSqMooUEDyyR93PWvQfDvxy1rw5p0VhFbNewRE7fttxvbB7bgoOKiLaSVjpqxhObkprV+f8AkfVdFfN3/DSmtf8AQAsP+/j1z/ib41ax4osGsbiGWzgZw5FlciMnHQbihOM89ad32I9nH+dfj/kdV8X/AIiRa3rLeGtKuIprC2gne5kVc75hFIMBvQD07568V4PWlFe6fbmR4LO6EjRSRgvdKwG5SucCMZ6+tZtCve4T5VBRTvq+/l3S7BRRRVGJ7lWl4e/5GXSv+vyH/wBDFZtaXh7/AJGXSv8Ar8h/9DFAGj48/wCR01D/ALZ/+i1rnK6Px5/yOmof9s//AEWtc5QB4bRRRQAUUUUAFFFFAHu3gCCVvh1pr2Vjb3RZ7gXTSoG2AMduWP3BjJyCOe/FdNdJbWttOfs9uoeS0jZzEpKK0BL7cjgk85HOa4r4S+FNS8Ti0DXktrottAWmKRAmWQyyfKrFSAQACfw45r3RPAXhtUVTp5YgYLGeTJ9+GrilQnJtq39fI+no5ph6NOFOfNolsl5P+b5bHFLBKmp3C3Njb2tkI7hYpYkClo/LboR98Y53HP15qr4ktbaC0Yx2zx4uNsD/AGZIlMeDwCHJkH3fmx+POK7yf4f+HZoHjS0kgZhgSRzOWX6biR+YNfP/AMQ7LxD4G1C7tzqDzW7yxNaXBt1GUYSbl+7gkFVzj29amWHna2n9fI0pZvh/aKS5tF2XTX+Znk9FFFd58oFFFFABRRRQB7lWl4e/5GXSv+vyH/0MVm1peHv+Rl0r/r8h/wDQxQBo+PP+R01D/tn/AOi1rnK6Px5/yOmof9s//Ra1zlAHhtdp8OvEq+EtZg1mRA8MN9CswxkiNkmViBkcgEke9cXV6L/kA3f/AF9Qf+gy1MtjWj8T9H+TPu6KWOaJJYnV43AZWU5BB6EGn1478B/Hi6zoQ8NXjYvdOjAhZnGZYc8ADr8owPpj1r2KqMgooooAKKKKACiiigAr5j+OnimHXtWuNPttrQ6XPFEZB/E7LIW5zggYH617V8S/G0PgjwpLecPe3GYbWMOFYsR97nsvXjPavkQzSXGkX88rF5JLyF3Y9yVlJNTLY1o/E/R/kzNoooqjIKKKKAPcq0vD3/Iy6V/1+Q/+his2tLw9/wAjLpX/AF+Q/wDoYoA0fHn/ACOmof8AbP8A9FrXOV0fjz/kdNQ/7Z/+i1rnKAPDaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD3KtLw9/yMulf9fkP/AKGKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/0Wtc5XR+PP+R01D/tn/wCi1rnKAPDaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD3KtLw9/wAjLpX/AF+Q/wDoYrNrS8Pf8jLpX/X5D/6GKANHx5/yOmof9s//AEWtc5XR+PP+R01D/tn/AOi1rnKAPDaKKKACiiigAooooA+rfgAo/wCFZRtgbjdygn8R/jXqVeL/ALP/AIgtf+EeHh53C3QVrxAxA3qzspwM5ONgzx3Fe0VMdjav8S9F+SCvDf2lFUaHob4G43LgnuQF/wDrmvcq+dv2hfENrqU0GkW7h302ZDMykEBpFf5eD1AQZB9aJbCo/E/R/kzwmiiiqMgooooAKKKKAPcq0vD3/Iy6V/1+Q/8AoYrNrS8Pf8jLpX/X5D/6GKANHx5/yOmof9s//Ra1zldH48/5HTUP+2f/AKLWucoA8Nq9F/yAbv8A6+oP/QZao1oWZgl0y5tpbqK3dpopFMiuQQqyA/dU/wB4VMtjah8dvJ/kyDT9Qu9K1CC/sZ3guoHDxyIcFTX1X8Nvi5pni+zt7HUZorTXM+WYjws5x95Py6evSvln7Bbf9Bey/wC+Jv8A43T47SGGVZYtatEkQhlZRMCpHcHy6OZB7GXdfev8z7uor5e8HfGHWvDYitdQ1iy1WwQBQsomEiKAAMN5fOAOh6k9a9Nj/aA8FkHzWvV9NsBb/CjmQexl3X3r/M9Uory7/hf/AIG/563/AP4Cn/Gmv+0B4JCny5L5m7BrcgfnzRzIPYy7r71/mep1ynjX4haH4IsXkv7hZL0xl4bND88p6D6DJ6n9a8j8X/HS71JXtvDd9Z6dARj7RMkrTHgdAEwuDn1yK8dvQ2o3T3N7r9vcTucs8nnsT+cdHMg9jLuvvX+ZN4s8Wap4y1yXVdVm3SN8sca/chTsqjsP59TzVGL/AJAN3/19Qf8AoMtH2C2/6C9l/wB8Tf8AxupJRb22kTQJew3Ekk8ThYlcYCrICTuUf3hSbuXTpuDbbWz6rs/MzaKKKs5gooooA9yrS8Pf8jLpX/X5D/6GKza0vD3/ACMulf8AX5D/AOhigDR8ef8AI6ah/wBs/wD0Wtc5XR+PP+R01D/tn/6LWucoA8NooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPcq0vD3/Iy6V/1+Q/8AoYrNrS8Pf8jLpX/X5D/6GKANHx5/yOmof9s//Ra1zldH48/5HTUP+2f/AKLWucoA8NooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPcq0vD3/ACMulf8AX5D/AOhis2tLw9/yMulf9fkP/oYoA0fHn/I6ah/2z/8ARa1zldH48/5HTUP+2f8A6LWucoA8NooooAKKKKACiiigDXstZjszZTRrew3lmhWO4tbsREZZmyPkJB+YjrXpmm/tD69YWSW8thDfMvSa6l+cj0JRVB+uM+9eOUVPKjb28+tvuX+R63rnx91/WrI2sdu2mqwIZ7CfY7f8CZWI6fw4rzW71CCa1miihuBJPMs0ks9wJCxAYdlHXeeazqKOVC9vPy+5f5BRRRVGQUUUUAFFFFAHuVaXh7/kZdK/6/If/QxWbWl4e/5GXSv+vyH/ANDFAGj48/5HTUP+2f8A6LWucro/Hn/I6ah/2z/9FrXOUAeG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHuVaXh7/kZdK/6/If/AEMVm1peHv8AkZdK/wCvyH/0MUAaPjz/AJHTUP8Atn/6LWucrsPGuj6pdeLr6a3028miby9rxwMynEajggetYP8Awj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAza0vD3/Iy6V/1+Q/+hij/hHta/6A+of+Az/4Vf0LQtXh8QabLLpV8kaXUTMzW7gKA4ySccCgB3jz/kdNQ/7Z/wDota5yuw8a6Pql14uvprfTbyaJvL2vHAzKcRqOCB61g/8ACPa1/wBAfUP/AAGf/CgDNorS/wCEe1r/AKA+of8AgM/+FH/CPa1/0B9Q/wDAZ/8ACgDNorS/4R7Wv+gPqH/gM/8AhR/wj2tf9AfUP/AZ/wDCgDNorS/4R7Wv+gPqH/gM/wDhR/wj2tf9AfUP/AZ/8KAM2itL/hHta/6A+of+Az/4Uf8ACPa1/wBAfUP/AAGf/CgDNorS/wCEe1r/AKA+of8AgM/+FH/CPa1/0B9Q/wDAZ/8ACgDNorS/4R7Wv+gPqH/gM/8AhR/wj2tf9AfUP/AZ/wDCgDNorS/4R7Wv+gPqH/gM/wDhR/wj2tf9AfUP/AZ/8KAM2itL/hHta/6A+of+Az/4Uf8ACPa1/wBAfUP/AAGf/CgDNrS8Pf8AIy6V/wBfkP8A6GKP+Ee1r/oD6h/4DP8A4Vf0LQtXh8QabLLpV8kaXUTMzW7gKA4ySccCgB3jz/kdNQ/7Z/8Aota5yuw8a6Pql14uvprfTbyaJvL2vHAzKcRqOCB61g/8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNrS8Pf8jLpX/X5D/wChij/hHta/6A+of+Az/wCFX9C0LV4fEGmyy6VfJGl1EzM1u4CgOMknHAoAd48/5HTUP+2f/ota5yuw8a6Pql14uvprfTbyaJvL2vHAzKcRqOCB61g/8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAzaK0v+Ee1r/oD6h/4DP8A4Uf8I9rX/QH1D/wGf/CgDNorS/4R7Wv+gPqH/gM/+FH/AAj2tf8AQH1D/wABn/woAzaK0v8AhHta/wCgPqH/AIDP/hR/wj2tf9AfUP8AwGf/AAoAzaK0v+Ee1r/oD6h/4DP/AIUf8I9rX/QH1D/wGf8AwoAza0vD3/Iy6V/1+Q/+hij/AIR7Wv8AoD6h/wCAz/4Vf0LQtXh8QabLLpV8kaXUTMzW7gKA4ySccCgDe8V+Ktf0/wAUXtlYXbLBEFZUEKNtHlqxOSpPcmsP/hPPEv8A0Ev/ACBH/wDE1d8VIknjvVg6qwFuWAIzgiAEGqE8kcMNzstLXMEUDITCpOWUbieOc578UAO/4TzxL/0Ev/IEf/xNH/CeeJf+gl/5Aj/+JqzFZxrqSx29lBLAbt1uN8YbYMjaMn7ox09enNcnQB0v/CceKAqsb9sP90/Z4+fp8tT/APCW+MjOIPPn84jd5f2RN2PXGzOKoxTyTzaJLJIilXOCVAUAPwMcCrUiSJbrFFpsrs0ci/Zp5CZAN6Nu4wSD0wMdCeaAGy+N/FUErRTXzxyL1V7aMEfgVpn/AAnniX/oJf8AkCP/AOJqtFZRDxFBGkO6ASRCRD8yozAEoT9cj8KsLEF8uS7s4EuRFcN5XlAAqqZQlfrnnvigBf8AhPPEv/QS/wDIEf8A8TR/wnniX/oJf+QI/wD4mmLEJUE8FrC949qrrGIgQTvKsQmMZwB29TWdrMaxao6LGkYCRkpH90HYucfjQBqf8J54l/6CX/kCP/4mj/hPPEv/AEEv/IEf/wATXOUUAdH/AMJ54l/6CX/kCP8A+Jo/4TzxL/0Ev/IEf/xNc5RQB0f/AAnniX/oJf8AkCP/AOJo/wCE88S/9BL/AMgR/wDxNc5RQB0f/CeeJf8AoJf+QI//AImj/hPPEv8A0Ev/ACBH/wDE1zlFAHR/8J54l/6CX/kCP/4mruj+NfEN1rmn282obopbmNHXyYxlSwBHC+lcfWl4e/5GXSv+vyH/ANDFAHW+K/FWv6f4ovbKwu2WCIKyoIUbaPLViclSe5NYf/CeeJf+gl/5Aj/+Jq74qRJPHerB1VgLcsARnBEAINUJ5I4YbnZaWuYIoGQmFScso3E8c5z34oAd/wAJ54l/6CX/AJAj/wDiaP8AhPPEv/QS/wDIEf8A8TVmKzjXUljt7KCWA3brcb4w2wZG0ZP3Rjp69Oa5RAxdQmSxPGOuaAOom8ZeLbdVae7liDjKl7VF3fTK1G3jnxQgUvfsocblJt4xkdMj5fY1DPZy2scdtqIuEjknElxcSI23cAflU4OScnJ5/EDNRa2rTrZSxyRzAWxJEKthFEjAdQMDkD8PpkAs/wDCeeJf+gl/5Aj/APiaP+E88S/9BL/yBH/8TVmC0Q3cST2NutrttzE3lgb3OzcM98gtke1UbdY7qK2nNtAbgidY0WMBXZQpUFehPJ+vHWgCX/hPPEv/AEEv/IEf/wATR/wnniX/AKCX/kCP/wCJrP1mMxvZ74I4ZWtw0iooX5tzdQOh6VmUAdH/AMJ54l/6CX/kCP8A+Jo/4TzxL/0Ev/IEf/xNc5RQB0f/AAnniX/oJf8AkCP/AOJo/wCE88S/9BL/AMgR/wDxNc5RQB0f/CeeJf8AoJf+QI//AImj/hPPEv8A0Ev/ACBH/wDE1zlFAHR/8J54l/6CX/kCP/4mj/hPPEv/AEEv/IEf/wATXOUUAdH/AMJ54l/6CX/kCP8A+Jq7o/jXxDda5p9vNqG6KW5jR18mMZUsARwvpXH1peHv+Rl0r/r8h/8AQxQB1vivxVr+n+KL2ysLtlgiCsqCFG2jy1YnJUnuTWH/AMJ54l/6CX/kCP8A+Jq74qRJPHerB1VgLcsARnBEAINUJ5I4YbnZaWuYIoGQmFScso3E8c5z34oAd/wnniX/AKCX/kCP/wCJo/4TzxL/ANBL/wAgR/8AxNWYrONdSWO3soJYDdutxvjDbBkbRk/dGOnr05rlY1VpUV32IWAZiM7R64oA6T/hOPFOxX+3ttckKfs8eCR1A+X3H50s3jXxXbPsnvZInxna9tGpx+K0rGEnSGgu7fyortxEArcAbDzuA5yMnpy3HtR1eB/stsEt54fL8xmilO51GR8xOB8pJwOOx65oAs/8J54l/wCgl/5Aj/8AiaP+E88S/wDQS/8AIEf/AMTVmC0Q3cST2NutrttzE3lgb3OzcM98gtke1UbdY7qK2nNtAbgidY0WMBXZQpUFehPJ+vHWgCX/AITzxL/0Ev8AyBH/APE0f8J54l/6CX/kCP8A+JrP1mMxvZ74I4ZWtw0iooX5tzdQOh6VmUAdH/wnniX/AKCX/kCP/wCJo/4TzxL/ANBL/wAgR/8AxNc5RQB0f/CeeJf+gl/5Aj/+Jo/4TzxL/wBBL/yBH/8AE1zlFAHR/wDCeeJf+gl/5Aj/APiaP+E88S/9BL/yBH/8TXOUUAdH/wAJ54l/6CX/AJAj/wDiaP8AhPPEv/QS/wDIEf8A8TXOUUAdH/wnniX/AKCX/kCP/wCJq7o/jXxDda5p9vNqG6KW5jR18mMZUsARwvpXH1peHv8AkZdK/wCvyH/0MUAdb4r8Va/p/ii9srC7ZYIgrKghRto8tWJyVJ7k1h/8J54l/wCgl/5Aj/8Aiau+KkSTx3qwdVYC3LAEZwRACDVCeSOGG52WlrmCKBkJhUnLKNxPHOc9+KAHf8J54l/6CX/kCP8A+Jo/4TzxL/0Ev/IEf/xNWYrONdSWO3soJYDdutxvjDbBkbRk/dGOnr05rlFGWA9/XFAHVS+LvGNuivNcTxo5wrPaIAx9srUc3jXxXbPsnvZInxna9tGpx+K0jRbtRivJ7VbG4N6pTc5YSLySeTggEKMjA5qnq8D/AGW2CW88Pl+YzRSnc6jI+YnA+Uk4HHY9c0AWf+E88S/9BL/yBH/8TR/wnniX/oJf+QI//iaswWiG7iSext1tdtuYm8sDe52bhnvkFsj2qjbrHdRW05toDcETrGixgK7KFKgr0J5P1460AS/8J54l/wCgl/5Aj/8AiaP+E88S/wDQS/8AIEf/AMTWfrMZjez3wRwytbhpFRQvzbm6gdD0rMoA6P8A4TzxL/0Ev/IEf/xNH/CeeJf+gl/5Aj/+JrnKKAOj/wCE88S/9BL/AMgR/wDxNH/CeeJf+gl/5Aj/APia5yigDo/+E88S/wDQS/8AIEf/AMTR/wAJ54l/6CX/AJAj/wDia5yigDo/+E88S/8AQS/8gR//ABNH/CeeJf8AoJf+QI//AImucooA6P8A4TzxL/0Ev/IEf/xNXdH8a+IbrXNPt5tQ3RS3MaOvkxjKlgCOF9K4+tLw9/yMulf9fkP/AKGKANHx5/yOmof9s/8A0Wtc5XR+PP8AkdNQ/wC2f/ota5ygDx6w1y+0yB4rNrePdn979liaVcjB2yFS68ehFTT+Jb65t5IHg0oJIpUmPSbVGAPoyxgg+4OayKKACiiigCeyvLjTr2G8tZPLuIHDxvgHaw6HB4qCiigC1YahNp05mgS2ZiMYuLaOdfydSM+9MvLy4v7uS6upTLPKcu57/wCH0qCigAooooAKKKKACiiigAooooA9yrS8Pf8AIy6V/wBfkP8A6GKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/wBFrXOV0fjz/kdNQ/7Z/wDota5ygDx6w1y+0yB4rNrePdn979liaVcjB2yFS68ehFTT+Jb65t5IHg0oJIpUmPSbVGAPoyxgg+4Oazbe1uLuQx20EszgbisaFiB64FWf7E1b/oGXv/gO/wDhSckt2aRpVJK8Yt/Io0Ve/sTVv+gZe/8AgO/+FH9iat/0DL3/AMB3/wAKXPHuV9Xq/wAr+5kF5eXF/cefcyeZLsRN2APlVQqjj0CgUWl1JZXKzxLCzr0E0KSr+KuCD+Iptxa3FpII7mCWFyNwWRCpI9cGoqrcyacXZlrUNRutUuftF3IHcKEUKioqqOgVVACj2AFVaKKBBRRRQAUUUUAFFFFABRRRQB7lWl4e/wCRl0r/AK/If/QxWbWl4e/5GXSv+vyH/wBDFAGj48/5HTUP+2f/AKLWucro/Hn/ACOmof8AbP8A9FrXOUAePWGuX2mQPFZtbx7s/vfssTSrkYO2QqXXj0IqafxLfXNvJA8GlBJFKkx6TaowB9GWMEH3BzWRRQAUUUUAT3l5cX9x59zJ5kuxE3YA+VVCqOPQKBRaXUllcrPEsLOvQTQpKv4q4IP4ioKKALWoajdapc/aLuQO4UIoVFRVUdAqqAFHsAKq0UUAFFFFABRRRQAUUUUAFFFFAHuVaXh7/kZdK/6/If8A0MVm1peHv+Rl0r/r8h/9DFAGj48/5HTUP+2f/ota5yuj8ef8jpqH/bP/ANFrXOUAePWGuX2mQPFZtbx7s/vfssTSrkYO2QqXXj0IqafxLfXNvJA8GlBJFKkx6TaowB9GWMEH3BzWRRQAUUUUAT3l5cX9x59zJ5kuxE3YA+VVCqOPQKBRaXUllcrPEsLOvQTQpKv4q4IP4inadbpd6naW0hYJNMkbFeoBYA4r0r/hVlquPN/tOENG0iGRcBlUZOPk/Xp6kVlUqxg7M7sLl9bFRcqdtO7PNtQ1G61S5+0XcgdwoRQqKiqo6BVUAKPYAVVr1MfCuFmiVYdWLSgmMBRlwOpHyc9R+dOHwptmjhKHUJJJXeMQpguCmM5Gz/aFR9Zh5nV/YmK7x+88qorr/FfhS00DT0mhe584ziJkmI44YngAc8VyFawmpq6PPxWFqYap7OpuFFFFWc4UUUUAFFFFAHuVaXh7/kZdK/6/If8A0MVm1peHv+Rl0r/r8h/9DFAGj48/5HTUP+2f/ota5yuj8ef8jpqH/bP/ANFrXOUAeG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHuVaXh7/AJGXSv8Ar8h/9DFZtaXh7/kZdK/6/If/AEMUAaPjz/kdNQ/7Z/8Aota5yuj8ef8AI6ah/wBs/wD0Wtc5QBwvwrtIrvxPdCdnEMVhLK4TG4hSpwM+vr2r1yx07T9QEawtcRwSXsMLBwhddyvnDY56ew9RwK8N8Ha3N4d1C81SBd8kNqQFyBuDSIpByCMYPTHNdQfjDqBkV/sIBR0kULIoClQQuAEwANx4HHNcdak5Tulc+ky7G06GGUJz5W9Vv38kz0J7bSo7OO5db0LcM6xBXVjGFA5bgbsk9BjA7mte48P2UjQyyyxwC4WNVP2iKFYsRJlirHL5JJIGPxJrnfh+3i7xXZiaw0yOy0pnbF3cTAgNwGMYKbs9srgZGCeK9KTwRqUedviEZ4wTYqShChQVJb5TgAZGDxWSoS6x/L/M7qma0G7wrd/5vl9np87/ADZ8wfEEY1+Af9Oy/wDoT1ydfWU/wQ8Oak4m1ia6urhRtV4m8oBeoGOeck8+9YWt/s+aWYnfQZIVkwNkV+ZWBOecujDt0+WuukpRgotHz2PlSr4mdSE1Zvz/AMj5qorpvEejX/hPU/7P1rw5a21xsDqDJKysp7qwkwfTjuCKx/t9t/0CLL/vub/45Wl32OT2cf51+P8AkUaKt6pDHbavewRLtjjndEXOcAMQBVSmndXM5xcJOL6BRRRTJCiiigAooooA9yrS8Pf8jLpX/X5D/wChis2tLw9/yMulf9fkP/oYoA0fHn/I6ah/2z/9FrXOV0fjz/kdNQ/7Z/8Aota5ygDw2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA9yrS8Pf8AIy6V/wBfkP8A6GKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/wBFrXOV0fjz/kdNQ/7Z/wDota5ygDw2iiigAooooA0vDziLxLpUjKGVbyFip74ccV9CyX1ol5e3H9qvMlytwyxbH+UujAbs8bskDjI96+dtE/5D2nf9fUX/AKEK+uPhl4CsfB/hm0L2sf8Aa0yCS5nZcsGI+6MjK4Bxj61hVpOpLfY9XA42OEpNuN+Ztb27eT/Q46TUba5nvIjd+Uk9rBEkzKxVSiplSAM4yp6DqBVu21awTT5LKa6W5eQyAzXKyhSMxkbth3YO09M9BkV6/WR4i8M6T4p0ubT9VtEmjkQqHwA6e6t2OQD+FZrDPv8Ah/wTrlncGkvZvS32l0Vv5T5u+LN0l5aJNGUZftEahkVlB2xEZwxJ7dScnrxXlFdD4v0S68N6nDo96uLi0jeNjggMPOkwwyAcEYIPcEVz1bUYcsbHm5hXVeqpJW0Xn0+QUUUVqcIUUUUAFFFFAHuVaXh7/kZdK/6/If8A0MVm1peHv+Rl0r/r8h/9DFAGj48/5HTUP+2f/ota5yuj8ef8jpqH/bP/ANFrXOUAeG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHuVaXh7/AJGXSv8Ar8h/9DFZtaXh7/kZdK/6/If/AEMUAaPjz/kdNQ/7Z/8Aota5yuj8ef8AI6ah/wBs/wD0Wtc5QB43Yf8AHlqn/Xqv/o6OvWvgv8K7fxDs8Sa5GJNPikxBaMvEzDu2eqj079+MivPvAmhnxLrzaOHVPtSIhZiRgedGT09ga+0LGyg06wgsrZAkEEYjRQOgAxUrdmtT4Yen6ssAADAGBRRRVGQUUUUAZXiLw7pvinRZ9K1S3Wa3lH4o3ZlPYivjzx74MuvA3iWXS55POiI8yCYDG9D0z7jvX2xXnfxo8LReIvh/e3PyrdaYjXkTnjhQS69M8rnA9cUAfKWt/wDIe1H/AK+pf/QjVGr2t/8AIe1H/r6l/wDQjVGph8KNcR/Fl6v8woooqjIKKKKACiiigD3KtLw9/wAjLpX/AF+Q/wDoYrNrS8Pf8jLpX/X5D/6GKANHx5/yOmof9s//AEWtc5XR+PP+R01D/tn/AOi1rnKAPDaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD3KtLw9/yMulf9fkP/oYrNrS8Pf8jLpX/X5D/wChigDR8ef8jpqH/bP/ANFrXOV0fjz/AJHTUP8Atn/6LWucoA8NooooAKKKKAL2if8AIe07/r6i/wDQhX3jXwdon/Ie07/r6i/9CFfVXwq+I+m+KvD9nYXN7EmtwIIpLeQhWl2g/MgJ+YYHPpjmpXxM2l/Cj6v8kej0UVz3izxpongzTJLvVbyNJNhaK2Vh5sxHGEXvyRz0GecVRifN/wAfP+SpXP8A16w/+g15jW54o1W6129t9VvWDXN3G8shHTJml4HoB0A9Kw6mOxtX+Jei/JBRRRVGIUUUUAFFFFAHuVaXh7/kZdK/6/If/QxWbWl4e/5GXSv+vyH/ANDFAGj48/5HTUP+2f8A6LWucro/Hn/I6ah/2z/9FrXOUAeG0UUUAFFFFABRRRQAUUUUAFFFe1QfDDTpWty2lMEmZB8lwWcBvukqXBGe2cA+tZVKqp2ujtweBnilJxkla27tvfbR9jxWivZo/hpp8sVvImlKVuATH/pRBIGctjfkAbTkngYqYfC7TfJY/wBnZmEiIqLOxVlZWbeHD4wAp56e/FZ/Wo9mdzyKst6kPvf+R4nRXpHi/wAH6doeiXMqWIiuECMjiZnBBYDIO4gjBPNeb1rTqKoro8/GYOeEmoTad1fTb+tAooorQ5D3KtLw9/yMulf9fkP/AKGKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/0Wtc5XR+PP+R01D/tn/wCi1rnKAOb+Af8AyVK2/wCvWb/0GvrGvk74B/8AJUrb/r1m/wDQa+salbs1qfDD0/VhRRRVGQUUUUAFI33T9KWkb7p+lAHwfrf/ACHtR/6+pf8A0I1Rq9rf/Ie1H/r6l/8AQjVGph8KNcR/Fl6v8woooqjIKKKKACiiigD3KtLw9/yMulf9fkP/AKGKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/0Wtc5XR+PP+R01D/tn/wCi1rnKAPDaKKKACiiigAorT8P6dHq+uW1jMZAku4Hy8buFJGMg9xXoD/Cy0h80T/2lC8aB9ki4JBYAfwccnvgfjxWVStGDszvwuW18VDnp2te2/p/meWUV6p/wqmMymIW+r+YF3lNgzt6Zxs6cdaX/AIVTbMIfJOoTmWHzgsOGKruK84TjlTUfWYeZ0/2Jie8fvPKqK6TxZ4etNB+yfZZJ387fu81gcY24xgD1Nc3W0JqceZHnYnDzw9V0qm6/4cKKKKowCiiigAooooA9yrS8Pf8AIy6V/wBfkP8A6GKza0vD3/Iy6V/1+Q/+higDR8ef8jpqH/bP/wBFrXOV0fjz/kdNQ/7Z/wDota5ygDw2iiigAooooAkgmktriOeJtskbB0bGcEHINWxq90DkR2Q/7cof/iKoUUnFPdFxqzgrRk0dd/wtHxx/0M1//wB9j/Csa78R6nqF1JdXskFzcSHLyzWsTux9SSuTWVRS5I9i/rFX+Z/eye6vJrxkaYp+7XYgSNUAGScYUAdST+NQUUVSVtjKUnJ3k7sKKKKBBRRRQAUUUUAe5VpeHv8AkZdK/wCvyH/0MVm1peHv+Rl0r/r8h/8AQxQBo+PP+R01D/tn/wCi1rnK6Px5/wAjpqH/AGz/APRa1zlAHhtFFFABRRRQAUUUUAFFFFABX0N/wlnhqLWINQnvwt3FJH5sPmxhUKYDYy2T06EDHqa+ea9P8DfDeTx9411V7iXydJs7tzcup+dsscIo9Tg89v0rGrT52kejgcW8LCckr3svlqd9pGqQas9smnpLfRx2T2k32QCVlyWORgnpuHXH9a2tPgutLimt4NF1eSOVhueWy3HGx1J2kEdWHynOcHmvTNN0qw0eyjs9OtIra3jUKqRLgcDAz6nA6mrlZrDW6nZPPHNNOmrPzfe54F448P634g0iW1ttNvlVIVVXmsfJUBX3n5Y1wB145Oe/NeHzaVHbzNFPqVrFIpwyPHOpH1Bjr7trhPiL8MtN8c6W/lpFa6tHloLoLjLej46g+vUfpWtOlyK1zhxWOWIkpOC0Vuvdvo13PkifT1itGuYr23uEWRY2EYcEFgxH3lH901SrYvNPudJtNV068QR3NrfxQyo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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
+        "def train_model(dataset, task, batch=8, epochs=7, imgsz=640):\n",
+        "    model_name = build_model_name('yolov8n', task)\n",
+        "    model = YOLO(f\"{model_name}.pt\")\n",
+        "    data = f\"{dataset}\" if task == 'classify' else f\"{dataset}/data.yaml\"\n",
+        "    results = model.train(data=data, batch=batch, epochs=epochs, imgsz=imgsz, device=\"cpu\")\n",
+        "    return model, model_name\n",
+        "\n",
+        "\n",
+        "def save_model(model, model_name, dataset, task, classes, imgsz=640):\n",
+        "    model_src = model.export(format=\"onnx\")\n",
+        "    multiple.upload_file(model_src, f\"{dataset}/{model_name}.onnx\")\n",
+        "    multiple.save_json(f\"{dataset}/{model_name}.json\", {'task': task, 'inputShape': [1, 3, imgsz, imgsz], 'classes': classes})\n",
+        "\n",
+        "\n",
+        "def plot_results(src, results):\n",
+        "    result = results[0]\n",
+        "    img = Image.open(src).convert('RGB')\n",
+        "    draw = ImageDraw.Draw(img)\n",
+        "    if result.masks:\n",
+        "        for mask in result.masks:\n",
+        "            print('mask', mask)\n",
+        "            polygon = mask.xy[0]\n",
+        "            draw.polygon(polygon, outline=\"#ffff00\", width=2)\n",
+        "    if result.boxes:\n",
+        "        for box in result.boxes.data:\n",
+        "            x1, y1, x2, y2, class_id, prob = box\n",
+        "            draw.rectangle((x1, y1, x2, y2), None, \"#00ff00\", width=3)\n",
+        "    if result.probs:\n",
+        "        draw.text((0, 0), f\"{result.names[result.probs.top1]} {result.probs.data[result.probs.top1]}\")\n",
+        "    display(img)\n",
+        "\n",
+        "\n",
+        "import ipywidgets as widgets\n",
+        "from IPython.display import display\n",
         "from IPython.display import Image as show_image\n",
         "\n",
-        "folder = 'segment' if task == 'segmentation' else 'detect'\n",
-        "show_image(filename=f\"runs/{folder}/train/val_batch0_labels.jpg\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 657
-        },
-        "id": "Yod0I4tHFOIQ",
-        "outputId": "29681842-1936-4ad4-f43f-9fe0ad2affce"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "show_image(filename=f\"runs/{folder}/train/val_batch0_pred.jpg\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "O4mtEgAGFeb4",
-        "outputId": "e4537bf4-e73f-43ac-de02-e03f402c03f1"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "image 1/1 /content/circles/test/images/img_136.png: 128x128 1 circle, 32.6ms\n",
-            "Speed: 2.1ms preprocess, 32.6ms inference, 1.4ms postprocess per image at shape (1, 3, 128, 128)\n"
-          ]
-        }
-      ],
-      "source": [
-        "import glob\n",
+        "projects = load_projects()\n",
         "\n",
-        "my_model = YOLO(f\"runs/{folder}/train/weights/best.pt\")\n",
+        "dropdown_project = widgets.Dropdown(description='Project', options=projects, value=projects[0])\n",
+        "text_classes = widgets.Text(description='Classes', value='')\n",
+        "text_task = widgets.Text(description='Task', value='')\n",
+        "label_status = widgets.Label(value='')\n",
         "\n",
-        "src = glob.glob(f\"{dataset}/test/images/*.png\")[0]\n",
-        "results = list(my_model(src, conf=0.128))\n",
-        "result = results[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 137
-        },
-        "id": "QqwjZTDFFhmb",
-        "outputId": "c6ebb524-3aee-4566-f865-b5c80c9f5ace"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHgAAAB4CAIAAAC2BqGFAAAAnElEQVR4nO3ZMQ6AIBBFQdZ4/ytja4uab3Rn+g3khWzDGPAndWVovnn4R21vX6ALoUP2O8NzfYlU1WOb51PSL3rOlpmtjhihQ4QOETpE6BChQ4QOETpE6BChQ4QOETpE6BChQ9Khqzr9X53cCr1arW3l4XM2xo4OERoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGjmAHcSCDd6/h/mAAAAAElFTkSuQmCC",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=120x120>"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from PIL import ImageDraw\n",
+        "def dropdown_project_eventhandler(change):\n",
+        "    output_train.clear_output()\n",
+        "    output_labels.clear_output()\n",
+        "    output_pred.clear_output()\n",
+        "\n",
+        "    label_status.value = 'Loading annotations...'\n",
+        "    dataset = dropdown_project.value\n",
+        "    annotations = load_annotations(dataset)\n",
+        "    classes = load_labels(annotations)\n",
+        "    task = load_task(annotations)\n",
+        "    text_classes.value = str(classes)\n",
+        "    text_task.value = str(task)\n",
+        "    label_status.value = 'Annotations loaded.'\n",
+        "\n",
+        "    button_train.disabled = False\n",
+        "\n",
+        "dropdown_project.observe(dropdown_project_eventhandler, names='value')\n",
+        "\n",
+        "button_train = widgets.Button(description='Train', disabled=True)\n",
+        "output_train = widgets.Output(layout={'border': '1px solid black', 'width': '50%', 'height': '360px', 'overflow': 'auto'})\n",
+        "\n",
+        "output_labels = widgets.Output()\n",
+        "output_pred = widgets.Output()\n",
         "\n",
-        "img = Image.open(src).convert('RGB')\n",
-        "draw = ImageDraw.Draw(img)\n",
-        "for box in result.boxes.data:\n",
-        "    x1, y1, x2, y2, class_id, prob = box\n",
-        "    draw.rectangle((x1, y1, x2, y2), None, \"#00ff00\", width=3)\n",
-        "if task == 'segmentation':\n",
-        "    polygon = result.masks.xy[0]\n",
-        "    draw.polygon(polygon, outline=\"#ffff00\", width=2)\n",
-        "img"
+        "def button_train_eventhandler(obj):\n",
+        "    output_train.clear_output()\n",
+        "    with output_train:\n",
+        "        button_train.disabled = True\n",
+        "        dataset = dropdown_project.value\n",
+        "        classes = eval(text_classes.value)\n",
+        "        task = text_task.value\n",
+        "        #model_path = f'{dropdown_project.value}/{text_model.value}.hdf5'\n",
+        "        #model_type = dropdown_type.value\n",
+        "        #batch_size = 32\n",
+        "        #epochs = 50\n",
+        "\n",
+        "        label_status.value = 'Downloading dataset...'\n",
+        "        create_dataset(dataset, task, classes)\n",
+        "\n",
+        "        image_size = 128\n",
+        "        label_status.value = 'Training model...'\n",
+        "        model, model_name = train_model(dataset, task, imgsz=image_size)\n",
+        "        save_model(model, model_name, dataset, task, classes, imgsz=image_size)\n",
+        "        label_status.value = 'Model saved'\n",
+        "\n",
+        "        src = glob.glob(f\"{dataset}/test/*/*.png\")[0]\n",
+        "        results = model.predict(src)\n",
+        "        plot_results(src, results)\n",
+        "    button_train.disabled = False\n",
+        "\n",
+        "    with output_labels:\n",
+        "        display(show_image(filename=f\"runs/{task}/train/val_batch0_labels.jpg\"))\n",
+        "    with output_pred:\n",
+        "        display(show_image(filename=f\"runs/{task}/train/val_batch0_pred.jpg\"))\n",
+        "\n",
+        "button_train.on_click(button_train_eventhandler)\n",
+        "\n",
+        "hbox_output = widgets.HBox([output_train, output_labels, output_pred])\n",
+        "dashboard = widgets.VBox([dropdown_project, text_classes, text_task, button_train, label_status, hbox_output])\n",
+        "display(dashboard)\n"
       ]
     }
   ],
@@ -752,23 +378,172 @@
     },
     "widgets": {
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+            "grid_auto_flow": null,
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           "model_module_version": "1.5.0",
-          "model_name": "ProgressStyleModel",
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             "_model_module": "@jupyter-widgets/controls",
             "_model_module_version": "1.5.0",
-            "_model_name": "ProgressStyleModel",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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+            "bar_style": "success",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_1cb82e807e1d4fc98348bc8e03e49dd8",
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+            "min": 0,
+            "orientation": "horizontal",
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+            "_model_module": "@jupyter-widgets/controls",
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+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_9be35863efb241bbac7f0f09b240ec4a",
+              "IPY_MODEL_ffaf128fbd4f4d6caeb22c871ada12af",
+              "IPY_MODEL_ea673f95f4c54105894b29bf407062d0"
+            ],
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+          }
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+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
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             "_view_count": null,
             "_view_module": "@jupyter-widgets/base",
             "_view_module_version": "1.2.0",
             "_view_name": "StyleView",
-            "bar_color": null,
             "description_width": ""
           }
         },
-        "16b6bae5e48941d8b476904650dd5f8c": {
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
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+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
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+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
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+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
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+          }
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+        "12ce5ed859d04c129eec58f8ea4a1c0f": {
           "model_module": "@jupyter-widgets/controls",
           "model_module_version": "1.5.0",
           "model_name": "HTMLModel",
@@ -783,64 +558,624 @@
             "_view_name": "HTMLView",
             "description": "",
             "description_tooltip": null,
-            "layout": "IPY_MODEL_fbb4cf5c26774cbdade6f7a6aafd78e5",
+            "layout": "IPY_MODEL_6f284d6270a54efcb9b11415fc73a5d7",
             "placeholder": "​",
-            "style": "IPY_MODEL_29d9e7741a2342e9bc339bce5188c500",
+            "style": "IPY_MODEL_0b6f6cd0d7b5473db3b0c5628168bb02",
             "value": "100%"
           }
         },
-        "270eabee3faa48d5bf6d37f8a0bbfd08": {
+        "17b9df8f825e4d6e92da34426a202f26": {
+          "model_module": "@jupyter-widgets/output",
+          "model_module_version": "1.0.0",
+          "model_name": "OutputModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/output",
+            "_view_module_version": "1.0.0",
+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_c887c46ab1ad483bbc0d602a7c05978d",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
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+            "_view_module": "@jupyter-widgets/base",
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+          "model_name": "DescriptionStyleModel",
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+            "_model_module_version": "1.5.0",
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+          "model_name": "DescriptionStyleModel",
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+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
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+          }
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+          "model_module": "@jupyter-widgets/output",
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+            "_model_module": "@jupyter-widgets/output",
+            "_model_module_version": "1.0.0",
+            "_model_name": "OutputModel",
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+            "_view_module": "@jupyter-widgets/output",
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+            "_view_name": "OutputView",
+            "layout": "IPY_MODEL_45735799812449028a7e0adc2c0a5695",
+            "msg_id": "",
+            "outputs": [
+              {
+                "data": {
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+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  " 22        [15, 18, 21]  1   1004275  ultralytics.nn.modules.head.Segment          [1, 32, 64, [64, 128, 256]]   \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "YOLOv8n-seg summary: 261 layers, 3,263,811 parameters, 3,263,795 gradients, 12.1 GFLOPs\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Transferred 381/417 items from pretrained weights\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/segment/train4', view at http://localhost:6006/\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Freezing layer 'model.22.dfl.conv.weight'\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/circles/train/labels...:   0%|          | 0/197 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/circles/train/labels... 164 images, 1 backgrounds, 0 corrupt:  83%|████████▎ | 164/197 [00:00<00:00, 1632.87it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/circles/train/labels... 197 images, 1 backgrounds, 0 corrupt: 100%|██████████| 197/197 [00:00<00:00, 1559.25it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/circles/train/labels.cache\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r\u001b[34m\u001b[1mval: \u001b[0mScanning /content/circles/val/labels...:   0%|          | 0/80 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r\u001b[34m\u001b[1mval: \u001b[0mScanning /content/circles/val/labels... 80 images, 0 backgrounds, 0 corrupt: 100%|██████████| 80/80 [00:00<00:00, 1757.15it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/circles/val/labels.cache\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Plotting labels to runs/segment/train4/labels.jpg... \n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Image sizes 128 train, 128 val\n",
+                  "Using 0 dataloader workers\n",
+                  "Logging results to \u001b[1mruns/segment/train4\u001b[0m\n",
+                  "Starting training for 7 epochs...\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        1/7         0G      1.579      2.151      3.705       1.02         11        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
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+              {
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+                  "\r        1/7         0G     0.8939     0.7402      2.443     0.9059         15        128: 100%|██████████| 25/25 [00:14<00:00,  1.75it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:02,  1.97it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:00<00:01,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:00,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:01<00:00,  2.15it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80     0.0957      0.974      0.732      0.513     0.0957      0.974      0.736      0.488\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.5968     0.5051      1.031     0.8298         19        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.5968     0.5051      1.031     0.8298         19        128:   4%|▍         | 1/25 [00:00<00:11,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6126     0.4545      1.002     0.8183         15        128:   4%|▍         | 1/25 [00:00<00:11,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6126     0.4545      1.002     0.8183         15        128:   8%|▊         | 2/25 [00:00<00:10,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.5862     0.3782     0.9872     0.8358         14        128:   8%|▊         | 2/25 [00:01<00:10,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.5862     0.3782     0.9872     0.8358         14        128:  12%|█▏        | 3/25 [00:01<00:11,  2.00it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6128     0.4442     0.9746     0.8389         17        128:  12%|█▏        | 3/25 [00:01<00:11,  2.00it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6128     0.4442     0.9746     0.8389         17        128:  16%|█▌        | 4/25 [00:01<00:10,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6279     0.4911     0.9322     0.8324         11        128:  16%|█▌        | 4/25 [00:02<00:10,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6279     0.4911     0.9322     0.8324         11        128:  20%|██        | 5/25 [00:02<00:09,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6321     0.5066     0.9445     0.8306         14        128:  20%|██        | 5/25 [00:02<00:09,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6321     0.5066     0.9445     0.8306         14        128:  24%|██▍       | 6/25 [00:02<00:09,  2.09it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6413      0.535     0.9215     0.8394         16        128:  24%|██▍       | 6/25 [00:03<00:09,  2.09it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6413      0.535     0.9215     0.8394         16        128:  28%|██▊       | 7/25 [00:03<00:08,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6794     0.5704     0.9258     0.8443         18        128:  28%|██▊       | 7/25 [00:03<00:08,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6794     0.5704     0.9258     0.8443         18        128:  32%|███▏      | 8/25 [00:03<00:07,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6906     0.5504     0.9288     0.8452         18        128:  32%|███▏      | 8/25 [00:04<00:07,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6906     0.5504     0.9288     0.8452         18        128:  36%|███▌      | 9/25 [00:04<00:07,  2.07it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6844     0.5334     0.9242     0.8462         14        128:  36%|███▌      | 9/25 [00:04<00:07,  2.07it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6844     0.5334     0.9242     0.8462         14        128:  40%|████      | 10/25 [00:04<00:06,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6876      0.535      0.915     0.8494         12        128:  40%|████      | 10/25 [00:05<00:06,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6876      0.535      0.915     0.8494         12        128:  44%|████▍     | 11/25 [00:05<00:06,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G       0.69     0.5072     0.9001     0.8527         12        128:  44%|████▍     | 11/25 [00:05<00:06,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G       0.69     0.5072     0.9001     0.8527         12        128:  48%|████▊     | 12/25 [00:05<00:05,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6944     0.4879     0.8865     0.8508         12        128:  48%|████▊     | 12/25 [00:06<00:05,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6944     0.4879     0.8865     0.8508         12        128:  52%|█████▏    | 13/25 [00:06<00:06,  1.94it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6881     0.4861     0.8761     0.8506         14        128:  52%|█████▏    | 13/25 [00:06<00:06,  1.94it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6881     0.4861     0.8761     0.8506         14        128:  56%|█████▌    | 14/25 [00:06<00:06,  1.80it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6813     0.5014     0.8712     0.8463         16        128:  56%|█████▌    | 14/25 [00:07<00:06,  1.80it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6813     0.5014     0.8712     0.8463         16        128:  60%|██████    | 15/25 [00:07<00:05,  1.71it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G      0.681      0.526     0.8655     0.8481         13        128:  60%|██████    | 15/25 [00:08<00:05,  1.71it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G      0.681      0.526     0.8655     0.8481         13        128:  64%|██████▍   | 16/25 [00:08<00:05,  1.64it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6748     0.5266     0.8539     0.8474         14        128:  64%|██████▍   | 16/25 [00:09<00:05,  1.64it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6748     0.5266     0.8539     0.8474         14        128:  68%|██████▊   | 17/25 [00:09<00:05,  1.48it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6673     0.5305     0.8432     0.8467         16        128:  68%|██████▊   | 17/25 [00:09<00:05,  1.48it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6673     0.5305     0.8432     0.8467         16        128:  72%|███████▏  | 18/25 [00:09<00:04,  1.45it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G      0.663     0.5438      0.837      0.849         16        128:  72%|███████▏  | 18/25 [00:10<00:04,  1.45it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G      0.663     0.5438      0.837      0.849         16        128:  76%|███████▌  | 19/25 [00:10<00:04,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6626     0.5352     0.8355     0.8498         13        128:  76%|███████▌  | 19/25 [00:11<00:04,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6626     0.5352     0.8355     0.8498         13        128:  80%|████████  | 20/25 [00:11<00:03,  1.54it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6672     0.5372     0.8348     0.8507         22        128:  80%|████████  | 20/25 [00:11<00:03,  1.54it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6672     0.5372     0.8348     0.8507         22        128:  84%|████████▍ | 21/25 [00:11<00:02,  1.64it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6699     0.5304     0.8363     0.8536          8        128:  84%|████████▍ | 21/25 [00:11<00:02,  1.64it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6699     0.5304     0.8363     0.8536          8        128:  88%|████████▊ | 22/25 [00:11<00:01,  1.79it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6672     0.5337     0.8302      0.854         18        128:  88%|████████▊ | 22/25 [00:12<00:01,  1.79it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6672     0.5337     0.8302      0.854         18        128:  92%|█████████▏| 23/25 [00:12<00:01,  1.90it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6664     0.5359     0.8262     0.8546         20        128:  92%|█████████▏| 23/25 [00:12<00:01,  1.90it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6664     0.5359     0.8262     0.8546         20        128:  96%|█████████▌| 24/25 [00:12<00:00,  1.99it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6665      0.548     0.8288     0.8533          9        128:  96%|█████████▌| 24/25 [00:13<00:00,  1.99it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6665      0.548     0.8288     0.8533          9        128: 100%|██████████| 25/25 [00:13<00:00,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        2/7         0G     0.6665      0.548     0.8288     0.8533          9        128: 100%|██████████| 25/25 [00:13<00:00,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:01,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:00<00:01,  2.05it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:00,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:01<00:00,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80          1      0.927      0.995      0.876          1      0.927      0.995      0.686\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
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+                  "\r        3/7         0G     0.6308     0.4969     0.7034     0.8771         11        128:  40%|████      | 10/25 [00:04<00:08,  1.87it/s]"
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+              {
+                "name": "stderr",
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+                  "\r        3/7         0G     0.6461     0.4855     0.7082     0.8837         12        128:  40%|████      | 10/25 [00:05<00:08,  1.87it/s]"
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+              {
+                "name": "stderr",
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+                  "\r        3/7         0G     0.6461     0.4855     0.7082     0.8837         12        128:  44%|████▍     | 11/25 [00:05<00:08,  1.73it/s]"
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+                "name": "stderr",
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+                  "\r        3/7         0G     0.6442     0.4617      0.711     0.8801         14        128:  44%|████▍     | 11/25 [00:06<00:08,  1.73it/s]"
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+              {
+                "name": "stderr",
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+                "text": [
+                  "\r        3/7         0G     0.6442     0.4617      0.711     0.8801         14        128:  48%|████▊     | 12/25 [00:06<00:07,  1.67it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6422     0.4576     0.7111     0.8821         15        128:  48%|████▊     | 12/25 [00:06<00:07,  1.67it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6422     0.4576     0.7111     0.8821         15        128:  52%|█████▏    | 13/25 [00:06<00:07,  1.59it/s]"
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+              {
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+                "text": [
+                  "\r        3/7         0G     0.6435     0.4745     0.7036     0.8859         14        128:  52%|█████▏    | 13/25 [00:07<00:07,  1.59it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6435     0.4745     0.7036     0.8859         14        128:  56%|█████▌    | 14/25 [00:07<00:07,  1.53it/s]"
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+              {
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+              {
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+                  "\r        3/7         0G     0.6454      0.472      0.701     0.8829         15        128:  60%|██████    | 15/25 [00:08<00:07,  1.41it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6389     0.4645     0.7011     0.8845          9        128:  60%|██████    | 15/25 [00:09<00:07,  1.41it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6389     0.4645     0.7011     0.8845          9        128:  64%|██████▍   | 16/25 [00:09<00:06,  1.44it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G      0.637     0.4587     0.7075      0.881         12        128:  64%|██████▍   | 16/25 [00:09<00:06,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G      0.637     0.4587     0.7075      0.881         12        128:  68%|██████▊   | 17/25 [00:09<00:04,  1.62it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6335     0.4549     0.7099     0.8797         16        128:  68%|██████▊   | 17/25 [00:09<00:04,  1.62it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6335     0.4549     0.7099     0.8797         16        128:  72%|███████▏  | 18/25 [00:09<00:03,  1.76it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G      0.625     0.4458     0.7034     0.8778         11        128:  72%|███████▏  | 18/25 [00:10<00:03,  1.76it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G      0.625     0.4458     0.7034     0.8778         11        128:  76%|███████▌  | 19/25 [00:10<00:03,  1.89it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6303     0.4459     0.7097     0.8793         10        128:  76%|███████▌  | 19/25 [00:11<00:03,  1.89it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6303     0.4459     0.7097     0.8793         10        128:  80%|████████  | 20/25 [00:11<00:03,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6406     0.4607     0.7155     0.8788         17        128:  80%|████████  | 20/25 [00:12<00:03,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6406     0.4607     0.7155     0.8788         17        128:  84%|████████▍ | 21/25 [00:12<00:03,  1.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6422     0.4534     0.7185     0.8747         12        128:  84%|████████▍ | 21/25 [00:13<00:03,  1.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6422     0.4534     0.7185     0.8747         12        128:  88%|████████▊ | 22/25 [00:13<00:02,  1.21it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6408     0.4501     0.7148     0.8738         17        128:  88%|████████▊ | 22/25 [00:14<00:02,  1.21it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6408     0.4501     0.7148     0.8738         17        128:  92%|█████████▏| 23/25 [00:14<00:01,  1.26it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6364     0.4443     0.7105     0.8721         13        128:  92%|█████████▏| 23/25 [00:14<00:01,  1.26it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6364     0.4443     0.7105     0.8721         13        128:  96%|█████████▌| 24/25 [00:14<00:00,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6373     0.4459     0.7134     0.8712         15        128:  96%|█████████▌| 24/25 [00:14<00:00,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6373     0.4459     0.7134     0.8712         15        128: 100%|██████████| 25/25 [00:14<00:00,  1.70it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        3/7         0G     0.6373     0.4459     0.7134     0.8712         15        128: 100%|██████████| 25/25 [00:14<00:00,  1.67it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:01,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:00<00:01,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:00,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:01<00:00,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.995          1      0.995      0.903      0.995          1      0.995      0.755\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6207     0.3128     0.6282     0.8961         19        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6207     0.3128     0.6282     0.8961         19        128:   4%|▍         | 1/25 [00:00<00:10,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6295     0.3687     0.6672     0.8764          9        128:   4%|▍         | 1/25 [00:01<00:10,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6295     0.3687     0.6672     0.8764          9        128:   8%|▊         | 2/25 [00:01<00:12,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6365     0.3588     0.6656     0.8707         19        128:   8%|▊         | 2/25 [00:01<00:12,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6365     0.3588     0.6656     0.8707         19        128:  12%|█▏        | 3/25 [00:01<00:13,  1.67it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6009     0.3189     0.6576     0.8811         13        128:  12%|█▏        | 3/25 [00:02<00:13,  1.67it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6009     0.3189     0.6576     0.8811         13        128:  16%|█▌        | 4/25 [00:02<00:13,  1.59it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6056     0.3366     0.6523     0.8697         17        128:  16%|█▌        | 4/25 [00:03<00:13,  1.59it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.6056     0.3366     0.6523     0.8697         17        128:  20%|██        | 5/25 [00:03<00:13,  1.53it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G       0.61     0.3914     0.6428     0.8608         19        128:  20%|██        | 5/25 [00:03<00:13,  1.53it/s]"
+                ]
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+                  "\r        4/7         0G     0.6079     0.3743     0.6497     0.8658         10        128:  28%|██▊       | 7/25 [00:04<00:12,  1.44it/s]"
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+                  "\r        4/7         0G     0.5946     0.3678     0.6748     0.8528          9        128:  28%|██▊       | 7/25 [00:05<00:12,  1.44it/s]"
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+                  "\r        4/7         0G     0.5946     0.3678     0.6748     0.8528          9        128:  32%|███▏      | 8/25 [00:05<00:11,  1.43it/s]"
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+                  "\r        4/7         0G     0.5846     0.3767     0.6664      0.848         14        128:  40%|████      | 10/25 [00:06<00:09,  1.65it/s]"
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+                  "\r        4/7         0G     0.5846     0.3767     0.6664      0.848         14        128:  44%|████▍     | 11/25 [00:06<00:07,  1.79it/s]"
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+                  "\r        4/7         0G     0.5841     0.3991     0.6644     0.8493         10        128:  44%|████▍     | 11/25 [00:07<00:07,  1.79it/s]"
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+                  "\r        4/7         0G     0.5841     0.3991     0.6644     0.8493         10        128:  48%|████▊     | 12/25 [00:07<00:06,  1.89it/s]"
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+              {
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+                "text": [
+                  "\r        4/7         0G     0.5876      0.388     0.6606     0.8514         14        128:  48%|████▊     | 12/25 [00:07<00:06,  1.89it/s]"
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+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5876      0.388     0.6606     0.8514         14        128:  52%|█████▏    | 13/25 [00:07<00:06,  1.93it/s]"
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+                "text": [
+                  "\r        4/7         0G     0.5857     0.3979     0.6637     0.8525          9        128:  52%|█████▏    | 13/25 [00:08<00:06,  1.93it/s]"
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+              {
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+                "text": [
+                  "\r        4/7         0G     0.5857     0.3979     0.6637     0.8525          9        128:  56%|█████▌    | 14/25 [00:08<00:05,  2.01it/s]"
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+              {
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+                  "\r        4/7         0G      0.578     0.3983     0.6606     0.8519         15        128:  60%|██████    | 15/25 [00:08<00:04,  2.04it/s]"
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+                "text": [
+                  "\r        4/7         0G     0.5832     0.4255     0.6612      0.854         18        128:  60%|██████    | 15/25 [00:09<00:04,  2.04it/s]"
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+              {
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+                "text": [
+                  "\r        4/7         0G     0.5832     0.4255     0.6612      0.854         18        128:  64%|██████▍   | 16/25 [00:09<00:04,  1.99it/s]"
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+              {
+                "name": "stderr",
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+                  "\r        4/7         0G     0.5801     0.4204     0.6563     0.8493         11        128:  64%|██████▍   | 16/25 [00:09<00:04,  1.99it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5801     0.4204     0.6563     0.8493         11        128:  68%|██████▊   | 17/25 [00:09<00:03,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5792     0.4192     0.6535     0.8507         18        128:  68%|██████▊   | 17/25 [00:10<00:03,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5792     0.4192     0.6535     0.8507         18        128:  72%|███████▏  | 18/25 [00:10<00:03,  2.05it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5769      0.426     0.6505     0.8542         12        128:  72%|███████▏  | 18/25 [00:10<00:03,  2.05it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5769      0.426     0.6505     0.8542         12        128:  76%|███████▌  | 19/25 [00:10<00:03,  1.92it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5781     0.4148      0.648     0.8544         13        128:  76%|███████▌  | 19/25 [00:11<00:03,  1.92it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5781     0.4148      0.648     0.8544         13        128:  80%|████████  | 20/25 [00:11<00:02,  1.76it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G      0.578     0.4141     0.6489     0.8537         18        128:  80%|████████  | 20/25 [00:12<00:02,  1.76it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G      0.578     0.4141     0.6489     0.8537         18        128:  84%|████████▍ | 21/25 [00:12<00:02,  1.66it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5779     0.4143     0.6477     0.8554         12        128:  84%|████████▍ | 21/25 [00:12<00:02,  1.66it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5779     0.4143     0.6477     0.8554         12        128:  88%|████████▊ | 22/25 [00:12<00:01,  1.57it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5852      0.423     0.6494     0.8572         21        128:  88%|████████▊ | 22/25 [00:13<00:01,  1.57it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5852      0.423     0.6494     0.8572         21        128:  92%|█████████▏| 23/25 [00:13<00:01,  1.52it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G      0.587     0.4159     0.6531      0.861         19        128:  92%|█████████▏| 23/25 [00:14<00:01,  1.52it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G      0.587     0.4159     0.6531      0.861         19        128:  96%|█████████▌| 24/25 [00:14<00:00,  1.42it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5914     0.4179     0.6596     0.8648          9        128:  96%|█████████▌| 24/25 [00:14<00:00,  1.42it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5914     0.4179     0.6596     0.8648          9        128: 100%|██████████| 25/25 [00:14<00:00,  1.54it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        4/7         0G     0.5914     0.4179     0.6596     0.8648          9        128: 100%|██████████| 25/25 [00:14<00:00,  1.68it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:02,  1.83it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:01<00:01,  1.65it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:01,  1.56it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:02<00:00,  1.51it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.46it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.52it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.991      0.975      0.995      0.864      0.991      0.975      0.995      0.742\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6929     0.4037     0.6201     0.9085         18        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6929     0.4037     0.6201     0.9085         18        128:   4%|▍         | 1/25 [00:00<00:15,  1.59it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G      0.617     0.3651     0.6183     0.8815         13        128:   4%|▍         | 1/25 [00:01<00:15,  1.59it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G      0.617     0.3651     0.6183     0.8815         13        128:   8%|▊         | 2/25 [00:01<00:12,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6225     0.3409     0.6366     0.9116         13        128:   8%|▊         | 2/25 [00:01<00:12,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6225     0.3409     0.6366     0.9116         13        128:  12%|█▏        | 3/25 [00:01<00:10,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6289     0.3798     0.6635     0.9127         13        128:  12%|█▏        | 3/25 [00:01<00:10,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6289     0.3798     0.6635     0.9127         13        128:  16%|█▌        | 4/25 [00:01<00:10,  2.09it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6086     0.3801     0.6555     0.8976         19        128:  16%|█▌        | 4/25 [00:02<00:10,  2.09it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6086     0.3801     0.6555     0.8976         19        128:  20%|██        | 5/25 [00:02<00:09,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5971     0.3715     0.6461     0.8912         13        128:  20%|██        | 5/25 [00:02<00:09,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5971     0.3715     0.6461     0.8912         13        128:  24%|██▍       | 6/25 [00:02<00:08,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6144     0.3555     0.6575     0.9047         10        128:  24%|██▍       | 6/25 [00:03<00:08,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6144     0.3555     0.6575     0.9047         10        128:  28%|██▊       | 7/25 [00:03<00:08,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6147     0.3677     0.6505     0.9023         12        128:  28%|██▊       | 7/25 [00:03<00:08,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6147     0.3677     0.6505     0.9023         12        128:  32%|███▏      | 8/25 [00:03<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6034     0.3819     0.6497     0.8957         17        128:  32%|███▏      | 8/25 [00:04<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.6034     0.3819     0.6497     0.8957         17        128:  36%|███▌      | 9/25 [00:04<00:07,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5979     0.3645     0.6493     0.8995         14        128:  36%|███▌      | 9/25 [00:04<00:07,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5979     0.3645     0.6493     0.8995         14        128:  40%|████      | 10/25 [00:04<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G      0.592     0.3548     0.6379     0.8946         14        128:  40%|████      | 10/25 [00:05<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G      0.592     0.3548     0.6379     0.8946         14        128:  44%|████▍     | 11/25 [00:05<00:06,  2.17it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5886     0.3619     0.6361     0.8922         12        128:  44%|████▍     | 11/25 [00:05<00:06,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5886     0.3619     0.6361     0.8922         12        128:  48%|████▊     | 12/25 [00:05<00:05,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5943     0.3678     0.6449     0.8916          8        128:  48%|████▊     | 12/25 [00:06<00:05,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5943     0.3678     0.6449     0.8916          8        128:  52%|█████▏    | 13/25 [00:06<00:05,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5901     0.3689     0.6357     0.8842         11        128:  52%|█████▏    | 13/25 [00:06<00:05,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5901     0.3689     0.6357     0.8842         11        128:  56%|█████▌    | 14/25 [00:06<00:05,  2.20it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5908     0.3626      0.627     0.8857         14        128:  56%|█████▌    | 14/25 [00:07<00:05,  2.20it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5908     0.3626      0.627     0.8857         14        128:  60%|██████    | 15/25 [00:07<00:04,  2.11it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5855     0.3643     0.6228     0.8852         13        128:  60%|██████    | 15/25 [00:07<00:04,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5855     0.3643     0.6228     0.8852         13        128:  64%|██████▍   | 16/25 [00:07<00:04,  2.15it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5832     0.3591     0.6225     0.8829         10        128:  64%|██████▍   | 16/25 [00:07<00:04,  2.15it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5832     0.3591     0.6225     0.8829         10        128:  68%|██████▊   | 17/25 [00:07<00:03,  2.20it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5809     0.3648     0.6254     0.8792         25        128:  68%|██████▊   | 17/25 [00:08<00:03,  2.20it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5809     0.3648     0.6254     0.8792         25        128:  72%|███████▏  | 18/25 [00:08<00:03,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5865     0.3705     0.6295     0.8776         13        128:  72%|███████▏  | 18/25 [00:08<00:03,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5865     0.3705     0.6295     0.8776         13        128:  76%|███████▌  | 19/25 [00:08<00:02,  2.23it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5896     0.3672     0.6284     0.8767         15        128:  76%|███████▌  | 19/25 [00:09<00:02,  2.23it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5896     0.3672     0.6284     0.8767         15        128:  80%|████████  | 20/25 [00:09<00:02,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5884     0.3717      0.627     0.8751         18        128:  80%|████████  | 20/25 [00:09<00:02,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5884     0.3717      0.627     0.8751         18        128:  84%|████████▍ | 21/25 [00:09<00:01,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5889     0.3742     0.6244     0.8726         15        128:  84%|████████▍ | 21/25 [00:10<00:01,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5889     0.3742     0.6244     0.8726         15        128:  88%|████████▊ | 22/25 [00:10<00:01,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5862     0.3772     0.6241     0.8724         14        128:  88%|████████▊ | 22/25 [00:10<00:01,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5862     0.3772     0.6241     0.8724         14        128:  92%|█████████▏| 23/25 [00:10<00:01,  1.96it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5853     0.3761     0.6227     0.8693         18        128:  92%|█████████▏| 23/25 [00:11<00:01,  1.96it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5853     0.3761     0.6227     0.8693         18        128:  96%|█████████▌| 24/25 [00:11<00:00,  1.78it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5834     0.3707     0.6241     0.8694         10        128:  96%|█████████▌| 24/25 [00:12<00:00,  1.78it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5834     0.3707     0.6241     0.8694         10        128: 100%|██████████| 25/25 [00:12<00:00,  1.90it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        5/7         0G     0.5834     0.3707     0.6241     0.8694         10        128: 100%|██████████| 25/25 [00:12<00:00,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:02,  1.39it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:01<00:02,  1.37it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:02<00:01,  1.36it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:02<00:00,  1.46it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.65it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.53it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.998          1      0.995      0.892      0.998          1      0.995      0.772\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5783      0.352     0.7041     0.8207         17        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5783      0.352     0.7041     0.8207         17        128:   4%|▍         | 1/25 [00:00<00:11,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5624     0.5032     0.6484     0.8352         11        128:   4%|▍         | 1/25 [00:00<00:11,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5624     0.5032     0.6484     0.8352         11        128:   8%|▊         | 2/25 [00:00<00:10,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5809     0.4821     0.6405     0.8586         10        128:   8%|▊         | 2/25 [00:01<00:10,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5809     0.4821     0.6405     0.8586         10        128:  12%|█▏        | 3/25 [00:01<00:09,  2.21it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5901     0.4892     0.6377     0.8624         12        128:  12%|█▏        | 3/25 [00:01<00:09,  2.21it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5901     0.4892     0.6377     0.8624         12        128:  16%|█▌        | 4/25 [00:01<00:09,  2.25it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5866     0.4485     0.6286     0.8629         19        128:  16%|█▌        | 4/25 [00:02<00:09,  2.25it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5866     0.4485     0.6286     0.8629         19        128:  20%|██        | 5/25 [00:02<00:09,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5893     0.4193     0.6328     0.8636         15        128:  20%|██        | 5/25 [00:02<00:09,  2.22it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5893     0.4193     0.6328     0.8636         15        128:  24%|██▍       | 6/25 [00:02<00:09,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.585     0.4066     0.6388      0.875          9        128:  24%|██▍       | 6/25 [00:03<00:09,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.585     0.4066     0.6388      0.875          9        128:  28%|██▊       | 7/25 [00:03<00:08,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5743     0.3805     0.6296     0.8739          9        128:  28%|██▊       | 7/25 [00:03<00:08,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5743     0.3805     0.6296     0.8739          9        128:  32%|███▏      | 8/25 [00:03<00:07,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5677      0.381     0.6173     0.8723         17        128:  32%|███▏      | 8/25 [00:04<00:07,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5677      0.381     0.6173     0.8723         17        128:  36%|███▌      | 9/25 [00:04<00:07,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.557     0.3846     0.6104     0.8689         15        128:  36%|███▌      | 9/25 [00:04<00:07,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.557     0.3846     0.6104     0.8689         15        128:  40%|████      | 10/25 [00:04<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5516     0.3832     0.6148     0.8698         18        128:  40%|████      | 10/25 [00:05<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5516     0.3832     0.6148     0.8698         18        128:  44%|████▍     | 11/25 [00:05<00:06,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5433     0.3682     0.6115     0.8691         13        128:  44%|████▍     | 11/25 [00:05<00:06,  2.19it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5433     0.3682     0.6115     0.8691         13        128:  48%|████▊     | 12/25 [00:05<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5459     0.3631     0.6091     0.8707         16        128:  48%|████▊     | 12/25 [00:05<00:06,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5459     0.3631     0.6091     0.8707         16        128:  52%|█████▏    | 13/25 [00:05<00:05,  2.20it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5413     0.3647     0.6148     0.8647         10        128:  52%|█████▏    | 13/25 [00:06<00:05,  2.20it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5413     0.3647     0.6148     0.8647         10        128:  56%|█████▌    | 14/25 [00:06<00:05,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5452     0.3622     0.6106     0.8603         16        128:  56%|█████▌    | 14/25 [00:06<00:05,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5452     0.3622     0.6106     0.8603         16        128:  60%|██████    | 15/25 [00:06<00:04,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5472     0.3718     0.6064     0.8638         17        128:  60%|██████    | 15/25 [00:07<00:04,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5472     0.3718     0.6064     0.8638         17        128:  64%|██████▍   | 16/25 [00:07<00:04,  2.14it/s]"
+                ]
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5481     0.3601     0.6039     0.8636         11        128:  68%|██████▊   | 17/25 [00:07<00:03,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5457     0.3569     0.5995     0.8632         13        128:  68%|██████▊   | 17/25 [00:08<00:03,  2.16it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5457     0.3569     0.5995     0.8632         13        128:  72%|███████▏  | 18/25 [00:08<00:03,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.543     0.3549     0.6001     0.8615         20        128:  72%|███████▏  | 18/25 [00:08<00:03,  2.18it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.543     0.3549     0.6001     0.8615         20        128:  76%|███████▌  | 19/25 [00:08<00:02,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.545     0.3598      0.602     0.8629         12        128:  76%|███████▌  | 19/25 [00:09<00:02,  2.02it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.545     0.3598      0.602     0.8629         12        128:  80%|████████  | 20/25 [00:09<00:02,  1.83it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5507     0.4474     0.6005     0.8623         13        128:  80%|████████  | 20/25 [00:10<00:02,  1.83it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5507     0.4474     0.6005     0.8623         13        128:  84%|████████▍ | 21/25 [00:10<00:02,  1.72it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.547     0.4426     0.5949     0.8614         10        128:  84%|████████▍ | 21/25 [00:11<00:02,  1.72it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.547     0.4426     0.5949     0.8614         10        128:  88%|████████▊ | 22/25 [00:11<00:01,  1.55it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5463     0.4532     0.5905     0.8625         15        128:  88%|████████▊ | 22/25 [00:11<00:01,  1.55it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G     0.5463     0.4532     0.5905     0.8625         15        128:  92%|█████████▏| 23/25 [00:11<00:01,  1.49it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.544       0.44     0.5887     0.8603         13        128:  92%|█████████▏| 23/25 [00:12<00:01,  1.49it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G      0.544       0.44     0.5887     0.8603         13        128:  96%|█████████▌| 24/25 [00:12<00:00,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G       0.54     0.4326     0.5895     0.8576         10        128:  96%|█████████▌| 24/25 [00:12<00:00,  1.44it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G       0.54     0.4326     0.5895     0.8576         10        128: 100%|██████████| 25/25 [00:12<00:00,  1.61it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        6/7         0G       0.54     0.4326     0.5895     0.8576         10        128: 100%|██████████| 25/25 [00:12<00:00,  1.93it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:01,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:00<00:01,  2.07it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:00,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:01<00:00,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.10it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.999          1      0.995      0.911      0.999          1      0.995      0.786\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r  0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4521     0.1946     0.5847     0.8732         15        128:   0%|          | 0/25 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4521     0.1946     0.5847     0.8732         15        128:   4%|▍         | 1/25 [00:00<00:11,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4688     0.2565     0.5753     0.8725         11        128:   4%|▍         | 1/25 [00:00<00:11,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4688     0.2565     0.5753     0.8725         11        128:   8%|▊         | 2/25 [00:00<00:10,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4781     0.2603     0.5823     0.8512         18        128:   8%|▊         | 2/25 [00:01<00:10,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4781     0.2603     0.5823     0.8512         18        128:  12%|█▏        | 3/25 [00:01<00:10,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4828     0.2726     0.5753     0.8449         13        128:  12%|█▏        | 3/25 [00:01<00:10,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4828     0.2726     0.5753     0.8449         13        128:  16%|█▌        | 4/25 [00:01<00:09,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4764     0.2469     0.5598     0.8391          9        128:  16%|█▌        | 4/25 [00:02<00:09,  2.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4764     0.2469     0.5598     0.8391          9        128:  20%|██        | 5/25 [00:02<00:09,  2.06it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G      0.477     0.2802     0.5598     0.8495         17        128:  20%|██        | 5/25 [00:02<00:09,  2.06it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G      0.477     0.2802     0.5598     0.8495         17        128:  24%|██▍       | 6/25 [00:02<00:08,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4748     0.2843     0.5623     0.8525         13        128:  24%|██▍       | 6/25 [00:03<00:08,  2.11it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4748     0.2843     0.5623     0.8525         13        128:  28%|██▊       | 7/25 [00:03<00:08,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4712     0.2741     0.5608     0.8471          9        128:  28%|██▊       | 7/25 [00:03<00:08,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4712     0.2741     0.5608     0.8471          9        128:  32%|███▏      | 8/25 [00:03<00:07,  2.15it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4845       0.29     0.5662     0.8479         19        128:  32%|███▏      | 8/25 [00:04<00:07,  2.15it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4845       0.29     0.5662     0.8479         19        128:  36%|███▌      | 9/25 [00:04<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4937     0.3144     0.5827     0.8611          7        128:  36%|███▌      | 9/25 [00:04<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4937     0.3144     0.5827     0.8611          7        128:  40%|████      | 10/25 [00:04<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4918     0.3073     0.5808     0.8678         11        128:  40%|████      | 10/25 [00:05<00:07,  2.13it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4918     0.3073     0.5808     0.8678         11        128:  44%|████▍     | 11/25 [00:05<00:06,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4884     0.3141     0.5856     0.8641         19        128:  44%|████▍     | 11/25 [00:05<00:06,  2.12it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4884     0.3141     0.5856     0.8641         19        128:  48%|████▊     | 12/25 [00:05<00:06,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4862     0.3132     0.5897     0.8636         18        128:  48%|████▊     | 12/25 [00:06<00:06,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4862     0.3132     0.5897     0.8636         18        128:  52%|█████▏    | 13/25 [00:06<00:05,  2.04it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4836     0.3111      0.586     0.8669         13        128:  52%|█████▏    | 13/25 [00:06<00:05,  2.04it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4836     0.3111      0.586     0.8669         13        128:  56%|█████▌    | 14/25 [00:06<00:05,  2.09it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.5084     0.3376     0.6054     0.8645         11        128:  56%|█████▌    | 14/25 [00:07<00:05,  2.09it/s]"
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+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.5084     0.3376     0.6054     0.8645         11        128:  60%|██████    | 15/25 [00:07<00:05,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4999     0.3723     0.5973     0.8598         12        128:  60%|██████    | 15/25 [00:07<00:05,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4999     0.3723     0.5973     0.8598         12        128:  64%|██████▍   | 16/25 [00:07<00:05,  1.74it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.5022     0.3881     0.5921     0.8596         16        128:  64%|██████▍   | 16/25 [00:08<00:05,  1.74it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.5022     0.3881     0.5921     0.8596         16        128:  68%|██████▊   | 17/25 [00:08<00:04,  1.64it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.5018     0.3774     0.5947     0.8665          8        128:  68%|██████▊   | 17/25 [00:09<00:04,  1.64it/s]"
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+              {
+                "name": "stderr",
+                "output_type": "stream",
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+                  "\r        7/7         0G     0.5018     0.3774     0.5947     0.8665          8        128:  72%|███████▏  | 18/25 [00:09<00:04,  1.56it/s]"
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+                "name": "stderr",
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+                "text": [
+                  "\r        7/7         0G     0.4932     0.3498     0.5803     0.8623         14        128:  88%|████████▊ | 22/25 [00:12<00:01,  1.59it/s]"
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+              {
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+                  "\r        7/7         0G     0.4943     0.3512     0.5844     0.8609         10        128:  92%|█████████▏| 23/25 [00:12<00:01,  1.71it/s]"
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+                  "\r        7/7         0G     0.4943     0.3512     0.5844     0.8609         10        128:  96%|█████████▌| 24/25 [00:12<00:00,  1.85it/s]"
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+              {
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+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4965     0.3458     0.5823     0.8565          5        128: 100%|██████████| 25/25 [00:13<00:00,  2.11it/s]"
+                ]
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+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r        7/7         0G     0.4965     0.3458     0.5823     0.8565          5        128: 100%|██████████| 25/25 [00:13<00:00,  1.88it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:01,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:00<00:01,  2.08it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:01<00:00,  2.06it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:01<00:00,  2.04it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.04it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:02<00:00,  2.05it/s]"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.999          1      0.995      0.947      0.999          1      0.995      0.755\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "7 epochs completed in 0.036 hours.\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Optimizer stripped from runs/segment/train4/weights/last.pt, 6.7MB\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Optimizer stripped from runs/segment/train4/weights/best.pt, 6.7MB\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "Validating runs/segment/train4/weights/best.pt...\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Ultralytics YOLOv8.2.84 🚀 Python-3.10.12 torch-2.4.0+cu121 CPU (Intel Xeon 2.20GHz)\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "YOLOv8n-seg summary (fused): 195 layers, 3,258,259 parameters, 0 gradients, 12.0 GFLOPs\n"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0%|          | 0/5 [00:00<?, ?it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  20%|██        | 1/5 [00:00<00:01,  2.14it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  40%|████      | 2/5 [00:01<00:02,  1.39it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  60%|██████    | 3/5 [00:02<00:01,  1.25it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):  80%|████████  | 4/5 [00:03<00:00,  1.17it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.28it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\r                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|██████████| 5/5 [00:03<00:00,  1.29it/s]"
+                ]
+              },
+              {
+                "name": "stderr",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "                   all         80         80      0.999          1      0.995      0.947      0.999          1      0.995      0.755\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Speed: 1.1ms preprocess, 30.7ms inference, 0.0ms loss, 0.6ms postprocess per image\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Results saved to \u001b[1mruns/segment/train4\u001b[0m\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "YOLOv8n-seg summary (fused): 195 layers, 3,258,259 parameters, 0 gradients, 12.0 GFLOPs\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'runs/segment/train4/weights/best.pt' with input shape (1, 3, 128, 128) BCHW and output shape(s) ((1, 37, 336), (1, 32, 32, 32)) (6.4 MB)\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.16.2 opset 19...\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 1.2s, saved as 'runs/segment/train4/weights/best.onnx' (12.5 MB)\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n",
+                  "Export complete (2.9s)\n",
+                  "Results saved to \u001b[1m/content/runs/segment/train4/weights\u001b[0m\n",
+                  "Predict:         yolo predict task=segment model=runs/segment/train4/weights/best.onnx imgsz=128  \n",
+                  "Validate:        yolo val task=segment model=runs/segment/train4/weights/best.onnx imgsz=128 data=circles/data.yaml  \n",
+                  "Visualize:       https://netron.app\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "image 1/1 /content/circles/test/images/img_36.png: 128x128 1 circle, 31.0ms\n"
+                ]
+              },
+              {
+                "name": "stdout",
+                "output_type": "stream",
+                "text": [
+                  "Speed: 0.6ms preprocess, 31.0ms inference, 1.5ms postprocess per image at shape (1, 3, 128, 128)\n"
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+              {
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+                "output_type": "stream",
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-            "layout": "IPY_MODEL_408c8214e9c84d469046e734469fb010",
+            "layout": "IPY_MODEL_c3eb4187087742ba82bef40f98fc4b60",
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-            "value": "100%"
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@@ -1777,6 +5977,30 @@
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+            "_view_module": "@jupyter-widgets/controls",
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