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voc_train.log
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2024-03-28 09:12:22,020 - train_voc_unicam.py - INFO: Pytorch version: 1.12.1
2024-03-28 09:12:22,054 - train_voc_unicam.py - INFO: GPU type: NVIDIA GeForce RTX 3090
2024-03-28 09:12:22,054 - train_voc_unicam.py - INFO:
args: Namespace(aux2final=8000, aux_layer=-3, backbone='vit_base_patch16_224', backend='nccl', betas=(0.9, 0.999), bkg_thre=0.5, cam_scales=(1.0, 0.5, 0.75, 1.5), ckpt_dir='w_outputs/2024-03/72,0_seed7_meansamples_28-09-12-22/checkpoints', crop_size=448, data_folder='/data/ziqing/Jaye_Files/Dataset/VOC2012', decoder='largefov', eval_iters=2000, high_thre=0.7, ignore_index=255, list_folder='datasets/voc', local_rank=0, log_iters=200, log_tag='72,0_seed7_meansamples', low_thre=0.25, lr=6e-05, max_iters=20000, momentum=0.9, num_classes=21, num_workers=10, optimizer='PolyWarmupAdamW', pooling='gmp', pooling_size=4, power=0.9, pred_dir='w_outputs/2024-03/72,0_seed7_meansamples_28-09-12-22/predictions', pretrained=True, radius=7, save_ckpt=True, scales=(0.5, 2), seed=7, spg=4, tb_dir='w_outputs/2024-03/72,0_seed7_meansamples_28-09-12-22/tensorboards', temp=0.5, tensorboard=False, train_set='train_aug', use_aa=False, use_gauss=False, use_solar=False, val_set='val', w_aff=0.1, w_kl=0.1, w_ptc=0.3, w_reg=0.05, w_seg=0.15, warmup_iters=1500, warmup_lr=1e-06, work_dir='w_outputs/2024-03/72,0_seed7_meansamples_28-09-12-22', wt_decay=0.01)
2024-03-28 09:12:22,055 - distributed_c10d.py - INFO: Added key: store_based_barrier_key:1 to store for rank: 0
2024-03-28 09:12:22,055 - distributed_c10d.py - INFO: Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
2024-03-28 09:12:22,055 - train_voc_unicam.py - INFO: Total gpus: 1, samples per gpu: 4...
2024-03-28 09:12:26,094 - train_voc_unicam.py - INFO:
Optimizer:
PolyWarmupAdamW (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 6e-05
maximize: False
weight_decay: 0.01
Parameter Group 1
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 6e-05
maximize: False
weight_decay: 0.01
Parameter Group 2
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.0006000000000000001
maximize: False
weight_decay: 0.01
Parameter Group 3
amsgrad: False
betas: (0.9, 0.999)
capturable: False
eps: 1e-08
foreach: None
lr: 0.0006000000000000001
maximize: False
weight_decay: 0.01
)
2024-03-28 09:18:02,619 - train_voc_unicam.py - INFO: Iter: 200; Elasped: 0:05:40; ETA: 9:21:00; LR: 7.960e-06; cls_loss: 0.5204, cls_loss_aux: 6.1908, ptc_loss: 0.4019, aff_loss: 0.8386, kl_loss: 0.0300, seg_loss: 3.0318...
2024-03-28 09:22:23,641 - train_voc_unicam.py - INFO: Iter: 400; Elasped: 0:10:01; ETA: 8:10:49; LR: 1.596e-05; cls_loss: 0.2344, cls_loss_aux: 0.3523, ptc_loss: 0.3929, aff_loss: 0.8377, kl_loss: 0.0349, seg_loss: 3.0258...
2024-03-28 09:26:43,119 - train_voc_unicam.py - INFO: Iter: 600; Elasped: 0:14:21; ETA: 7:43:59; LR: 2.396e-05; cls_loss: 0.1957, cls_loss_aux: 0.2522, ptc_loss: 0.3738, aff_loss: 0.8344, kl_loss: 0.0483, seg_loss: 3.0058...
2024-03-28 09:31:06,137 - train_voc_unicam.py - INFO: Iter: 800; Elasped: 0:18:44; ETA: 7:29:36; LR: 3.196e-05; cls_loss: 0.1425, cls_loss_aux: 0.2013, ptc_loss: 0.3653, aff_loss: 0.8204, kl_loss: 0.0620, seg_loss: 2.6647...
2024-03-28 09:35:35,315 - train_voc_unicam.py - INFO: Iter: 1000; Elasped: 0:23:13; ETA: 7:21:07; LR: 3.996e-05; cls_loss: 0.1152, cls_loss_aux: 0.1801, ptc_loss: 0.3564, aff_loss: 0.8132, kl_loss: 0.0543, seg_loss: 2.5411...
2024-03-28 09:40:02,210 - train_voc_unicam.py - INFO: Iter: 1200; Elasped: 0:27:40; ETA: 7:13:26; LR: 4.796e-05; cls_loss: 0.0953, cls_loss_aux: 0.1875, ptc_loss: 0.3352, aff_loss: 0.8023, kl_loss: 0.0527, seg_loss: 2.3844...
2024-03-28 09:44:31,333 - train_voc_unicam.py - INFO: Iter: 1400; Elasped: 0:32:09; ETA: 7:07:08; LR: 5.596e-05; cls_loss: 0.0885, cls_loss_aux: 0.1606, ptc_loss: 0.2579, aff_loss: 0.7909, kl_loss: 0.0467, seg_loss: 2.5731...
2024-03-28 09:48:59,589 - train_voc_unicam.py - INFO: Iter: 1600; Elasped: 0:36:37; ETA: 7:01:05; LR: 5.566e-05; cls_loss: 0.0784, cls_loss_aux: 0.1527, ptc_loss: 0.1599, aff_loss: 0.7876, kl_loss: 0.0416, seg_loss: 2.9162...
2024-03-28 09:53:26,562 - train_voc_unicam.py - INFO: Iter: 1800; Elasped: 0:41:04; ETA: 6:55:13; LR: 5.512e-05; cls_loss: 0.0862, cls_loss_aux: 0.1765, ptc_loss: 0.1340, aff_loss: 0.7862, kl_loss: 0.0573, seg_loss: 3.0154...
2024-03-28 09:57:54,958 - train_voc_unicam.py - INFO: Iter: 2000; Elasped: 0:45:32; ETA: 6:49:48; LR: 5.457e-05; cls_loss: 0.0864, cls_loss_aux: 0.1561, ptc_loss: 0.1103, aff_loss: 0.7845, kl_loss: 0.0492, seg_loss: 3.0236...
2024-03-28 09:57:54,958 - train_voc_unicam.py - INFO: Validating...
2024-03-28 10:04:41,135 - train_voc_unicam.py - INFO: val cls score: 0.830732
2024-03-28 10:04:41,135 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 76.832 | 80.425 | 77.566 | 75.234 | 0.489 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 34.866 | 37.105 | 37.186 | 33.317 | 0.487 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 31.997 | 40.538 | 31.615 | 29.960 | 0.011 |
+------------------+--------+---------+---------+--------+----------+
| bird | 45.548 | 50.584 | 51.018 | 43.569 | 0.002 |
+------------------+--------+---------+---------+--------+----------+
| boat | 31.564 | 40.249 | 33.554 | 30.335 | 0 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 51.008 | 47.581 | 51.827 | 49.579 | 0 |
+------------------+--------+---------+---------+--------+----------+
| bus | 72.697 | 68.211 | 72.190 | 71.353 | 0.000 |
+------------------+--------+---------+---------+--------+----------+
| car | 64.123 | 58.438 | 65.767 | 63.159 | 0.103 |
+------------------+--------+---------+---------+--------+----------+
| cat | 76.586 | 64.382 | 74.796 | 77.437 | 0.054 |
+------------------+--------+---------+---------+--------+----------+
| chair | 24.952 | 22.739 | 26.274 | 24.574 | 0.279 |
+------------------+--------+---------+---------+--------+----------+
| cow | 66.448 | 2.783 | 68.536 | 67.268 | 0.116 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 45.119 | 32.932 | 45.034 | 45.866 | 0.033 |
+------------------+--------+---------+---------+--------+----------+
| dog | 71.810 | 61.703 | 70.409 | 72.617 | 1.021 |
+------------------+--------+---------+---------+--------+----------+
| horse | 65.708 | 55.179 | 63.939 | 62.821 | 0.000 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 61.313 | 58.445 | 62.137 | 59.877 | 1.373 |
+------------------+--------+---------+---------+--------+----------+
| person | 65.171 | 61.161 | 64.599 | 65.399 | 2.415 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 15.124 | 42.204 | 14.732 | 14.397 | 0.029 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 69.863 | 66.699 | 68.463 | 70.101 | 0 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 51.437 | 55.245 | 50.907 | 52.031 | 0.636 |
+------------------+--------+---------+---------+--------+----------+
| train | 47.815 | 48.877 | 47.741 | 46.396 | 0.007 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 30.445 | 35.570 | 30.365 | 30.167 | 0.453 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 60.023 | 63.541 | 60.925 | 58.247 | 5.817 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 84.631 | 69.623 | 84.130 | 86.512 | 2.676 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 1.086 | 0.767 | 1.052 | 1.172 | inf |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 52.401 | 49.098 | 52.793 | 51.689 | 0.358 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 10:09:08,511 - train_voc_unicam.py - INFO: Iter: 2200; Elasped: 0:56:46; ETA: 7:39:17; LR: 5.403e-05; cls_loss: 0.0944, cls_loss_aux: 0.1527, ptc_loss: 0.1098, aff_loss: 0.7881, kl_loss: 0.0443, seg_loss: 1.0353...
2024-03-28 10:13:35,654 - train_voc_unicam.py - INFO: Iter: 2400; Elasped: 1:01:13; ETA: 7:28:55; LR: 5.348e-05; cls_loss: 0.0836, cls_loss_aux: 0.1461, ptc_loss: 0.1107, aff_loss: 0.7858, kl_loss: 0.0419, seg_loss: 0.3348...
2024-03-28 10:18:03,653 - train_voc_unicam.py - INFO: Iter: 2600; Elasped: 1:05:41; ETA: 7:19:34; LR: 5.293e-05; cls_loss: 0.0856, cls_loss_aux: 0.1506, ptc_loss: 0.0988, aff_loss: 0.7828, kl_loss: 0.0422, seg_loss: 0.3280...
2024-03-28 10:22:33,680 - train_voc_unicam.py - INFO: Iter: 2800; Elasped: 1:10:11; ETA: 7:11:07; LR: 5.239e-05; cls_loss: 0.0778, cls_loss_aux: 0.1365, ptc_loss: 0.0882, aff_loss: 0.7810, kl_loss: 0.0401, seg_loss: 0.2414...
2024-03-28 10:27:00,902 - train_voc_unicam.py - INFO: Iter: 3000; Elasped: 1:14:38; ETA: 7:02:55; LR: 5.184e-05; cls_loss: 0.0705, cls_loss_aux: 0.1256, ptc_loss: 0.0715, aff_loss: 0.7789, kl_loss: 0.0383, seg_loss: 0.1806...
2024-03-28 10:31:31,236 - train_voc_unicam.py - INFO: Iter: 3200; Elasped: 1:19:09; ETA: 6:55:32; LR: 5.129e-05; cls_loss: 0.0680, cls_loss_aux: 0.1180, ptc_loss: 0.0759, aff_loss: 0.7798, kl_loss: 0.0375, seg_loss: 0.1795...
2024-03-28 10:35:59,014 - train_voc_unicam.py - INFO: Iter: 3400; Elasped: 1:23:37; ETA: 6:48:14; LR: 5.074e-05; cls_loss: 0.0723, cls_loss_aux: 0.1230, ptc_loss: 0.0745, aff_loss: 0.7782, kl_loss: 0.0390, seg_loss: 0.2031...
2024-03-28 10:40:27,916 - train_voc_unicam.py - INFO: Iter: 3600; Elasped: 1:28:05; ETA: 6:41:16; LR: 5.019e-05; cls_loss: 0.0716, cls_loss_aux: 0.1267, ptc_loss: 0.0671, aff_loss: 0.7793, kl_loss: 0.0362, seg_loss: 0.2182...
2024-03-28 10:44:55,774 - train_voc_unicam.py - INFO: Iter: 3800; Elasped: 1:32:33; ETA: 6:34:33; LR: 4.964e-05; cls_loss: 0.0713, cls_loss_aux: 0.1245, ptc_loss: 0.0640, aff_loss: 0.7787, kl_loss: 0.0355, seg_loss: 0.1634...
2024-03-28 10:49:23,788 - train_voc_unicam.py - INFO: Iter: 4000; Elasped: 1:37:01; ETA: 6:28:04; LR: 4.909e-05; cls_loss: 0.0735, cls_loss_aux: 0.1240, ptc_loss: 0.0670, aff_loss: 0.7784, kl_loss: 0.0371, seg_loss: 0.1486...
2024-03-28 10:49:23,789 - train_voc_unicam.py - INFO: Validating...
2024-03-28 10:56:09,632 - train_voc_unicam.py - INFO: val cls score: 0.887057
2024-03-28 10:56:09,632 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 89.282 | 88.071 | 89.077 | 88.725 | 87.731 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 78.002 | 76.570 | 77.352 | 75.038 | 78.420 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 41.804 | 44.418 | 40.932 | 39.333 | 39.805 |
+------------------+--------+---------+---------+--------+----------+
| bird | 75.690 | 82.823 | 77.020 | 73.537 | 81.188 |
+------------------+--------+---------+---------+--------+----------+
| boat | 67.555 | 65.005 | 65.342 | 64.040 | 60.753 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 69.093 | 59.928 | 68.666 | 67.854 | 64.333 |
+------------------+--------+---------+---------+--------+----------+
| bus | 83.951 | 76.421 | 84.054 | 84.943 | 72.683 |
+------------------+--------+---------+---------+--------+----------+
| car | 83.279 | 72.624 | 83.506 | 83.015 | 75.933 |
+------------------+--------+---------+---------+--------+----------+
| cat | 86.616 | 80.245 | 86.736 | 86.851 | 83.948 |
+------------------+--------+---------+---------+--------+----------+
| chair | 34.979 | 29.864 | 31.908 | 34.743 | 19.334 |
+------------------+--------+---------+---------+--------+----------+
| cow | 84.364 | 66.967 | 84.066 | 84.427 | 78.566 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 62.230 | 55.827 | 60.455 | 63.279 | 56.716 |
+------------------+--------+---------+---------+--------+----------+
| dog | 85.882 | 80.596 | 86.173 | 86.176 | 78.650 |
+------------------+--------+---------+---------+--------+----------+
| horse | 81.988 | 81.130 | 79.277 | 79.384 | 66.428 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 77.283 | 71.923 | 76.167 | 76.876 | 69.086 |
+------------------+--------+---------+---------+--------+----------+
| person | 72.014 | 67.712 | 71.492 | 73.161 | 68.516 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 43.778 | 42.910 | 41.248 | 42.303 | 40.465 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 86.368 | 78.766 | 85.049 | 85.061 | 80.931 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 58.900 | 67.167 | 58.128 | 59.933 | 37.336 |
+------------------+--------+---------+---------+--------+----------+
| train | 55.026 | 58.441 | 54.546 | 54.125 | 46.638 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 38.488 | 52.650 | 37.371 | 36.948 | 46.252 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 76.223 | 77.910 | 75.574 | 74.248 | 73.671 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 87.731 | 82.287 | 86.839 | 89.265 | 81.300 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.412 | 0.377 | 0.430 | 0.460 | 0.541 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 69.361 | 66.669 | 68.503 | 68.560 | 63.510 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 11:00:37,099 - train_voc_unicam.py - INFO: Iter: 4200; Elasped: 1:48:15; ETA: 6:47:13; LR: 4.853e-05; cls_loss: 0.0780, cls_loss_aux: 0.1319, ptc_loss: 0.0689, aff_loss: 0.7791, kl_loss: 0.0383, seg_loss: 0.1993...
2024-03-28 11:05:06,067 - train_voc_unicam.py - INFO: Iter: 4400; Elasped: 1:52:44; ETA: 6:39:41; LR: 4.798e-05; cls_loss: 0.0751, cls_loss_aux: 0.1400, ptc_loss: 0.0705, aff_loss: 0.7757, kl_loss: 0.0407, seg_loss: 0.1984...
2024-03-28 11:09:33,511 - train_voc_unicam.py - INFO: Iter: 4600; Elasped: 1:57:11; ETA: 6:32:18; LR: 4.743e-05; cls_loss: 0.0716, cls_loss_aux: 0.1147, ptc_loss: 0.0571, aff_loss: 0.7767, kl_loss: 0.0347, seg_loss: 0.1398...
2024-03-28 11:14:02,675 - train_voc_unicam.py - INFO: Iter: 4800; Elasped: 2:01:40; ETA: 6:25:16; LR: 4.687e-05; cls_loss: 0.0788, cls_loss_aux: 0.1270, ptc_loss: 0.0594, aff_loss: 0.7772, kl_loss: 0.0359, seg_loss: 0.1943...
2024-03-28 11:18:30,840 - train_voc_unicam.py - INFO: Iter: 5000; Elasped: 2:06:08; ETA: 6:18:24; LR: 4.632e-05; cls_loss: 0.0727, cls_loss_aux: 0.1113, ptc_loss: 0.0585, aff_loss: 0.7774, kl_loss: 0.0342, seg_loss: 0.1281...
2024-03-28 11:22:58,088 - train_voc_unicam.py - INFO: Iter: 5200; Elasped: 2:10:36; ETA: 6:11:42; LR: 4.576e-05; cls_loss: 0.0691, cls_loss_aux: 0.0971, ptc_loss: 0.0562, aff_loss: 0.7770, kl_loss: 0.0339, seg_loss: 0.1155...
2024-03-28 11:27:29,465 - train_voc_unicam.py - INFO: Iter: 5400; Elasped: 2:15:07; ETA: 6:05:18; LR: 4.520e-05; cls_loss: 0.0596, cls_loss_aux: 0.0886, ptc_loss: 0.0560, aff_loss: 0.7747, kl_loss: 0.0348, seg_loss: 0.1320...
2024-03-28 11:31:57,415 - train_voc_unicam.py - INFO: Iter: 5600; Elasped: 2:19:35; ETA: 5:58:55; LR: 4.465e-05; cls_loss: 0.0569, cls_loss_aux: 0.0836, ptc_loss: 0.0579, aff_loss: 0.7723, kl_loss: 0.0341, seg_loss: 0.1230...
2024-03-28 11:36:25,866 - train_voc_unicam.py - INFO: Iter: 5800; Elasped: 2:24:03; ETA: 5:52:40; LR: 4.409e-05; cls_loss: 0.0623, cls_loss_aux: 0.0873, ptc_loss: 0.0548, aff_loss: 0.7727, kl_loss: 0.0328, seg_loss: 0.1332...
2024-03-28 11:40:52,967 - train_voc_unicam.py - INFO: Iter: 6000; Elasped: 2:28:30; ETA: 5:46:30; LR: 4.353e-05; cls_loss: 0.0601, cls_loss_aux: 0.0818, ptc_loss: 0.0531, aff_loss: 0.7713, kl_loss: 0.0350, seg_loss: 0.1330...
2024-03-28 11:40:52,967 - train_voc_unicam.py - INFO: Validating...
2024-03-28 11:47:40,536 - train_voc_unicam.py - INFO: val cls score: 0.893440
2024-03-28 11:47:40,536 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 89.789 | 89.573 | 89.874 | 89.127 | 89.572 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 72.960 | 80.461 | 73.516 | 70.339 | 72.381 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 45.932 | 42.337 | 45.139 | 44.447 | 42.016 |
+------------------+--------+---------+---------+--------+----------+
| bird | 78.406 | 84.752 | 79.711 | 76.632 | 80.555 |
+------------------+--------+---------+---------+--------+----------+
| boat | 60.174 | 65.721 | 62.629 | 58.163 | 62.400 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 68.724 | 68.843 | 67.963 | 66.943 | 66.960 |
+------------------+--------+---------+---------+--------+----------+
| bus | 84.617 | 79.845 | 84.286 | 83.305 | 80.118 |
+------------------+--------+---------+---------+--------+----------+
| car | 84.213 | 67.623 | 84.306 | 84.133 | 80.980 |
+------------------+--------+---------+---------+--------+----------+
| cat | 86.730 | 78.541 | 85.259 | 87.240 | 83.147 |
+------------------+--------+---------+---------+--------+----------+
| chair | 41.426 | 35.563 | 37.795 | 41.505 | 28.838 |
+------------------+--------+---------+---------+--------+----------+
| cow | 87.206 | 77.412 | 87.304 | 86.976 | 84.061 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 61.976 | 55.987 | 60.996 | 63.244 | 56.508 |
+------------------+--------+---------+---------+--------+----------+
| dog | 85.084 | 81.683 | 84.635 | 84.399 | 78.426 |
+------------------+--------+---------+---------+--------+----------+
| horse | 85.326 | 70.789 | 84.129 | 84.868 | 80.625 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 80.601 | 76.057 | 79.298 | 79.525 | 75.748 |
+------------------+--------+---------+---------+--------+----------+
| person | 79.786 | 76.061 | 79.123 | 78.790 | 77.897 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 53.721 | 53.001 | 53.728 | 53.325 | 45.043 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 83.061 | 84.653 | 83.473 | 81.366 | 73.009 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 53.877 | 67.280 | 52.465 | 53.984 | 40.337 |
+------------------+--------+---------+---------+--------+----------+
| train | 58.520 | 62.769 | 59.260 | 57.948 | 55.741 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 42.230 | 61.399 | 41.897 | 40.736 | 39.618 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 76.797 | 79.744 | 76.899 | 75.087 | 75.037 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 89.147 | 83.778 | 88.203 | 90.333 | 83.409 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.366 | 0.307 | 0.364 | 0.403 | 0.401 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 70.684 | 69.540 | 70.323 | 69.857 | 66.380 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 11:52:07,481 - train_voc_unicam.py - INFO: Iter: 6200; Elasped: 2:39:45; ETA: 5:55:34; LR: 4.297e-05; cls_loss: 0.0609, cls_loss_aux: 0.0851, ptc_loss: 0.0581, aff_loss: 0.7726, kl_loss: 0.0345, seg_loss: 0.1797...
2024-03-28 11:56:35,998 - train_voc_unicam.py - INFO: Iter: 6400; Elasped: 2:44:13; ETA: 5:48:57; LR: 4.241e-05; cls_loss: 0.0564, cls_loss_aux: 0.0776, ptc_loss: 0.0497, aff_loss: 0.7687, kl_loss: 0.0338, seg_loss: 0.1093...
2024-03-28 12:01:05,978 - train_voc_unicam.py - INFO: Iter: 6600; Elasped: 2:48:43; ETA: 5:42:32; LR: 4.185e-05; cls_loss: 0.0632, cls_loss_aux: 0.0886, ptc_loss: 0.0492, aff_loss: 0.7701, kl_loss: 0.0335, seg_loss: 0.1115...
2024-03-28 12:05:35,123 - train_voc_unicam.py - INFO: Iter: 6800; Elasped: 2:53:13; ETA: 5:36:14; LR: 4.128e-05; cls_loss: 0.0560, cls_loss_aux: 0.0856, ptc_loss: 0.0519, aff_loss: 0.7697, kl_loss: 0.0334, seg_loss: 0.1455...
2024-03-28 12:10:01,760 - train_voc_unicam.py - INFO: Iter: 7000; Elasped: 2:57:39; ETA: 5:29:55; LR: 4.072e-05; cls_loss: 0.0590, cls_loss_aux: 0.0786, ptc_loss: 0.0481, aff_loss: 0.7721, kl_loss: 0.0337, seg_loss: 0.1130...
2024-03-28 12:14:29,898 - train_voc_unicam.py - INFO: Iter: 7200; Elasped: 3:02:07; ETA: 5:23:45; LR: 4.016e-05; cls_loss: 0.0654, cls_loss_aux: 0.0894, ptc_loss: 0.0541, aff_loss: 0.7702, kl_loss: 0.0345, seg_loss: 0.1243...
2024-03-28 12:18:57,486 - train_voc_unicam.py - INFO: Iter: 7400; Elasped: 3:06:35; ETA: 5:17:41; LR: 3.959e-05; cls_loss: 0.0641, cls_loss_aux: 0.0844, ptc_loss: 0.0488, aff_loss: 0.7698, kl_loss: 0.0336, seg_loss: 0.1070...
2024-03-28 12:23:25,947 - train_voc_unicam.py - INFO: Iter: 7600; Elasped: 3:11:03; ETA: 5:11:42; LR: 3.902e-05; cls_loss: 0.0611, cls_loss_aux: 0.0816, ptc_loss: 0.0518, aff_loss: 0.7698, kl_loss: 0.0333, seg_loss: 0.1395...
2024-03-28 12:27:53,443 - train_voc_unicam.py - INFO: Iter: 7800; Elasped: 3:15:31; ETA: 5:05:48; LR: 3.846e-05; cls_loss: 0.0629, cls_loss_aux: 0.0787, ptc_loss: 0.0498, aff_loss: 0.7710, kl_loss: 0.0326, seg_loss: 0.1147...
2024-03-28 12:32:22,259 - train_voc_unicam.py - INFO: Iter: 8000; Elasped: 3:20:00; ETA: 5:00:00; LR: 3.789e-05; cls_loss: 0.0598, cls_loss_aux: 0.0751, ptc_loss: 0.0517, aff_loss: 0.7703, kl_loss: 0.0326, seg_loss: 0.1095...
2024-03-28 12:32:22,260 - train_voc_unicam.py - INFO: Validating...
2024-03-28 12:39:09,569 - train_voc_unicam.py - INFO: val cls score: 0.907033
2024-03-28 12:39:09,570 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.013 | 88.613 | 91.031 | 90.565 | 90.206 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 76.818 | 80.526 | 77.224 | 74.674 | 71.638 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 40.565 | 39.911 | 39.322 | 38.469 | 36.793 |
+------------------+--------+---------+---------+--------+----------+
| bird | 80.472 | 80.998 | 81.252 | 78.674 | 81.070 |
+------------------+--------+---------+---------+--------+----------+
| boat | 62.407 | 64.451 | 63.532 | 61.128 | 54.225 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 71.034 | 57.328 | 70.381 | 69.330 | 70.649 |
+------------------+--------+---------+---------+--------+----------+
| bus | 86.748 | 76.661 | 86.504 | 86.464 | 84.161 |
+------------------+--------+---------+---------+--------+----------+
| car | 82.287 | 58.997 | 82.291 | 82.548 | 77.752 |
+------------------+--------+---------+---------+--------+----------+
| cat | 89.857 | 83.306 | 88.345 | 89.799 | 86.227 |
+------------------+--------+---------+---------+--------+----------+
| chair | 42.519 | 37.653 | 40.929 | 42.142 | 30.971 |
+------------------+--------+---------+---------+--------+----------+
| cow | 89.161 | 82.227 | 88.608 | 88.874 | 85.243 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 63.324 | 55.742 | 61.207 | 65.632 | 58.098 |
+------------------+--------+---------+---------+--------+----------+
| dog | 86.035 | 82.300 | 85.426 | 85.337 | 81.433 |
+------------------+--------+---------+---------+--------+----------+
| horse | 86.974 | 70.832 | 85.311 | 86.218 | 81.657 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 77.080 | 73.145 | 74.971 | 76.397 | 70.778 |
+------------------+--------+---------+---------+--------+----------+
| person | 81.568 | 76.636 | 80.186 | 81.069 | 78.139 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 57.238 | 48.692 | 54.940 | 55.867 | 50.268 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 87.954 | 82.351 | 86.190 | 86.879 | 85.094 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 54.084 | 64.928 | 53.315 | 54.364 | 40.532 |
+------------------+--------+---------+---------+--------+----------+
| train | 58.271 | 54.854 | 58.214 | 57.868 | 55.529 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 60.506 | 61.228 | 62.262 | 59.991 | 57.685 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 79.113 | 78.106 | 79.217 | 77.601 | 75.940 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 89.396 | 83.196 | 88.039 | 90.458 | 85.035 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.326 | 0.354 | 0.324 | 0.358 | 0.406 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 72.663 | 67.685 | 71.973 | 72.014 | 68.007 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 12:43:38,318 - train_voc_unicam.py - INFO: Iter: 8200; Elasped: 3:31:16; ETA: 5:04:01; LR: 3.732e-05; cls_loss: 0.0504, cls_loss_aux: 0.0603, ptc_loss: 0.0496, aff_loss: 0.7662, kl_loss: 0.0243, seg_loss: 0.0907...
2024-03-28 12:48:05,745 - train_voc_unicam.py - INFO: Iter: 8400; Elasped: 3:35:43; ETA: 4:57:53; LR: 3.675e-05; cls_loss: 0.0582, cls_loss_aux: 0.0688, ptc_loss: 0.0526, aff_loss: 0.7684, kl_loss: 0.0217, seg_loss: 0.0894...
2024-03-28 12:52:33,831 - train_voc_unicam.py - INFO: Iter: 8600; Elasped: 3:40:11; ETA: 4:51:52; LR: 3.618e-05; cls_loss: 0.0500, cls_loss_aux: 0.0591, ptc_loss: 0.0441, aff_loss: 0.7675, kl_loss: 0.0227, seg_loss: 0.1204...
2024-03-28 12:57:02,122 - train_voc_unicam.py - INFO: Iter: 8800; Elasped: 3:44:40; ETA: 4:45:56; LR: 3.561e-05; cls_loss: 0.0499, cls_loss_aux: 0.0605, ptc_loss: 0.0455, aff_loss: 0.7675, kl_loss: 0.0214, seg_loss: 0.0947...
2024-03-28 13:01:30,167 - train_voc_unicam.py - INFO: Iter: 9000; Elasped: 3:49:08; ETA: 4:40:03; LR: 3.504e-05; cls_loss: 0.0572, cls_loss_aux: 0.0687, ptc_loss: 0.0456, aff_loss: 0.7661, kl_loss: 0.0201, seg_loss: 0.0937...
2024-03-28 13:05:57,564 - train_voc_unicam.py - INFO: Iter: 9200; Elasped: 3:53:35; ETA: 4:34:12; LR: 3.446e-05; cls_loss: 0.0522, cls_loss_aux: 0.0662, ptc_loss: 0.0435, aff_loss: 0.7649, kl_loss: 0.0205, seg_loss: 0.0955...
2024-03-28 13:10:25,183 - train_voc_unicam.py - INFO: Iter: 9400; Elasped: 3:58:03; ETA: 4:28:26; LR: 3.389e-05; cls_loss: 0.0553, cls_loss_aux: 0.0697, ptc_loss: 0.0449, aff_loss: 0.7658, kl_loss: 0.0203, seg_loss: 0.0882...
2024-03-28 13:14:53,919 - train_voc_unicam.py - INFO: Iter: 9600; Elasped: 4:02:31; ETA: 4:22:43; LR: 3.331e-05; cls_loss: 0.0537, cls_loss_aux: 0.0616, ptc_loss: 0.0425, aff_loss: 0.7632, kl_loss: 0.0208, seg_loss: 0.0973...
2024-03-28 13:19:20,764 - train_voc_unicam.py - INFO: Iter: 9800; Elasped: 4:06:58; ETA: 4:17:02; LR: 3.273e-05; cls_loss: 0.0498, cls_loss_aux: 0.0579, ptc_loss: 0.0414, aff_loss: 0.7651, kl_loss: 0.0205, seg_loss: 0.0980...
2024-03-28 13:23:48,342 - train_voc_unicam.py - INFO: Iter: 10000; Elasped: 4:11:26; ETA: 4:11:26; LR: 3.216e-05; cls_loss: 0.0520, cls_loss_aux: 0.0659, ptc_loss: 0.0419, aff_loss: 0.7661, kl_loss: 0.0195, seg_loss: 0.1044...
2024-03-28 13:23:48,343 - train_voc_unicam.py - INFO: Validating...
2024-03-28 13:30:34,954 - train_voc_unicam.py - INFO: val cls score: 0.892409
2024-03-28 13:30:34,955 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 90.910 | 87.081 | 90.783 | 90.806 | 90.418 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 81.884 | 78.152 | 80.921 | 80.069 | 80.630 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 43.729 | 36.027 | 42.243 | 42.377 | 38.886 |
+------------------+--------+---------+---------+--------+----------+
| bird | 85.783 | 84.169 | 85.270 | 84.552 | 85.667 |
+------------------+--------+---------+---------+--------+----------+
| boat | 69.264 | 63.485 | 69.050 | 68.460 | 65.085 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 78.017 | 47.676 | 79.253 | 78.870 | 76.548 |
+------------------+--------+---------+---------+--------+----------+
| bus | 86.998 | 76.492 | 86.995 | 87.087 | 85.250 |
+------------------+--------+---------+---------+--------+----------+
| car | 80.319 | 55.918 | 80.089 | 80.992 | 78.825 |
+------------------+--------+---------+---------+--------+----------+
| cat | 84.250 | 76.963 | 83.624 | 85.367 | 83.492 |
+------------------+--------+---------+---------+--------+----------+
| chair | 44.434 | 33.775 | 43.857 | 45.280 | 29.838 |
+------------------+--------+---------+---------+--------+----------+
| cow | 76.998 | 46.818 | 76.463 | 79.907 | 40.415 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 42.382 | 49.443 | 41.147 | 45.169 | 37.712 |
+------------------+--------+---------+---------+--------+----------+
| dog | 82.710 | 77.573 | 82.680 | 84.192 | 77.232 |
+------------------+--------+---------+---------+--------+----------+
| horse | 86.748 | 77.833 | 85.415 | 85.864 | 57.121 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 77.189 | 73.388 | 75.237 | 76.373 | 72.430 |
+------------------+--------+---------+---------+--------+----------+
| person | 80.978 | 74.032 | 79.263 | 80.953 | 78.672 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 53.845 | 43.518 | 50.462 | 53.546 | 50.650 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 86.724 | 80.175 | 85.108 | 86.996 | 82.455 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 57.152 | 67.398 | 56.857 | 56.815 | 43.781 |
+------------------+--------+---------+---------+--------+----------+
| train | 59.566 | 56.843 | 58.375 | 59.243 | 57.532 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 56.679 | 66.142 | 57.483 | 55.475 | 52.896 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 82.496 | 76.037 | 82.489 | 81.125 | 79.546 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 84.037 | 80.383 | 82.861 | 85.695 | 78.141 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.256 | 0.414 | 0.257 | 0.283 | 0.323 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 71.741 | 64.424 | 70.980 | 71.828 | 65.026 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 13:35:02,194 - train_voc_unicam.py - INFO: Iter: 10200; Elasped: 4:22:40; ETA: 4:12:21; LR: 3.158e-05; cls_loss: 0.0570, cls_loss_aux: 0.0640, ptc_loss: 0.0446, aff_loss: 0.7646, kl_loss: 0.0198, seg_loss: 0.0970...
2024-03-28 13:39:30,227 - train_voc_unicam.py - INFO: Iter: 10400; Elasped: 4:27:08; ETA: 4:06:35; LR: 3.100e-05; cls_loss: 0.0493, cls_loss_aux: 0.0563, ptc_loss: 0.0379, aff_loss: 0.7638, kl_loss: 0.0193, seg_loss: 0.0773...
2024-03-28 13:43:59,957 - train_voc_unicam.py - INFO: Iter: 10600; Elasped: 4:31:37; ETA: 4:00:52; LR: 3.041e-05; cls_loss: 0.0398, cls_loss_aux: 0.0453, ptc_loss: 0.0363, aff_loss: 0.7631, kl_loss: 0.0195, seg_loss: 0.0803...
2024-03-28 13:48:28,548 - train_voc_unicam.py - INFO: Iter: 10800; Elasped: 4:36:06; ETA: 3:55:11; LR: 2.983e-05; cls_loss: 0.0424, cls_loss_aux: 0.0451, ptc_loss: 0.0416, aff_loss: 0.7608, kl_loss: 0.0190, seg_loss: 0.0961...
2024-03-28 13:52:57,365 - train_voc_unicam.py - INFO: Iter: 11000; Elasped: 4:40:35; ETA: 3:49:34; LR: 2.925e-05; cls_loss: 0.0444, cls_loss_aux: 0.0493, ptc_loss: 0.0393, aff_loss: 0.7624, kl_loss: 0.0189, seg_loss: 0.0832...
2024-03-28 13:57:26,363 - train_voc_unicam.py - INFO: Iter: 11200; Elasped: 4:45:04; ETA: 3:43:58; LR: 2.866e-05; cls_loss: 0.0407, cls_loss_aux: 0.0439, ptc_loss: 0.0364, aff_loss: 0.7617, kl_loss: 0.0188, seg_loss: 0.0801...
2024-03-28 14:01:54,312 - train_voc_unicam.py - INFO: Iter: 11400; Elasped: 4:49:32; ETA: 3:38:25; LR: 2.807e-05; cls_loss: 0.0431, cls_loss_aux: 0.0493, ptc_loss: 0.0387, aff_loss: 0.7616, kl_loss: 0.0187, seg_loss: 0.0738...
2024-03-28 14:06:22,752 - train_voc_unicam.py - INFO: Iter: 11600; Elasped: 4:54:00; ETA: 3:32:53; LR: 2.749e-05; cls_loss: 0.0515, cls_loss_aux: 0.0539, ptc_loss: 0.0425, aff_loss: 0.7639, kl_loss: 0.0188, seg_loss: 0.0821...
2024-03-28 14:10:49,282 - train_voc_unicam.py - INFO: Iter: 11800; Elasped: 4:58:27; ETA: 3:27:23; LR: 2.690e-05; cls_loss: 0.0437, cls_loss_aux: 0.0476, ptc_loss: 0.0411, aff_loss: 0.7623, kl_loss: 0.0185, seg_loss: 0.0885...
2024-03-28 14:15:17,206 - train_voc_unicam.py - INFO: Iter: 12000; Elasped: 5:02:55; ETA: 3:21:56; LR: 2.631e-05; cls_loss: 0.0470, cls_loss_aux: 0.0513, ptc_loss: 0.0436, aff_loss: 0.7626, kl_loss: 0.0180, seg_loss: 0.0815...
2024-03-28 14:15:17,207 - train_voc_unicam.py - INFO: Validating...
2024-03-28 14:22:06,103 - train_voc_unicam.py - INFO: val cls score: 0.903811
2024-03-28 14:22:06,104 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.477 | 88.916 | 91.476 | 91.135 | 90.865 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 82.244 | 77.562 | 80.295 | 80.082 | 82.020 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 40.180 | 38.441 | 39.928 | 38.528 | 37.280 |
+------------------+--------+---------+---------+--------+----------+
| bird | 83.395 | 83.995 | 83.783 | 81.931 | 84.414 |
+------------------+--------+---------+---------+--------+----------+
| boat | 68.987 | 63.708 | 69.720 | 67.149 | 64.334 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 72.771 | 54.243 | 71.355 | 71.569 | 71.110 |
+------------------+--------+---------+---------+--------+----------+
| bus | 84.246 | 78.810 | 85.122 | 83.424 | 81.907 |
+------------------+--------+---------+---------+--------+----------+
| car | 81.268 | 62.937 | 80.687 | 81.423 | 78.977 |
+------------------+--------+---------+---------+--------+----------+
| cat | 88.576 | 81.004 | 87.064 | 88.700 | 85.911 |
+------------------+--------+---------+---------+--------+----------+
| chair | 49.602 | 35.301 | 48.778 | 50.333 | 35.299 |
+------------------+--------+---------+---------+--------+----------+
| cow | 88.830 | 70.662 | 87.769 | 89.032 | 84.936 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 51.982 | 46.180 | 50.200 | 53.841 | 46.342 |
+------------------+--------+---------+---------+--------+----------+
| dog | 88.449 | 82.464 | 87.872 | 88.496 | 82.594 |
+------------------+--------+---------+---------+--------+----------+
| horse | 87.597 | 78.932 | 86.277 | 86.878 | 83.084 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 75.866 | 69.654 | 74.770 | 75.886 | 72.896 |
+------------------+--------+---------+---------+--------+----------+
| person | 80.685 | 79.373 | 80.661 | 79.761 | 77.258 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 58.692 | 52.933 | 54.046 | 58.716 | 53.058 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 88.377 | 82.605 | 87.387 | 87.320 | 84.281 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 60.081 | 63.877 | 58.822 | 60.453 | 50.117 |
+------------------+--------+---------+---------+--------+----------+
| train | 61.834 | 65.762 | 62.419 | 61.156 | 60.310 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 65.488 | 66.758 | 65.276 | 65.222 | 59.257 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 82.506 | 78.297 | 82.864 | 81.168 | 80.913 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 87.394 | 82.773 | 85.885 | 88.370 | 82.907 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.257 | 0.348 | 0.251 | 0.282 | 0.288 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 73.839 | 67.815 | 73.034 | 73.383 | 69.821 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 14:26:35,494 - train_voc_unicam.py - INFO: Iter: 12200; Elasped: 5:14:13; ETA: 3:20:53; LR: 2.571e-05; cls_loss: 0.0448, cls_loss_aux: 0.0467, ptc_loss: 0.0393, aff_loss: 0.7601, kl_loss: 0.0183, seg_loss: 0.0764...
2024-03-28 14:31:04,225 - train_voc_unicam.py - INFO: Iter: 12400; Elasped: 5:18:42; ETA: 3:15:19; LR: 2.512e-05; cls_loss: 0.0398, cls_loss_aux: 0.0424, ptc_loss: 0.0399, aff_loss: 0.7621, kl_loss: 0.0174, seg_loss: 0.0701...
2024-03-28 14:35:32,184 - train_voc_unicam.py - INFO: Iter: 12600; Elasped: 5:23:10; ETA: 3:09:47; LR: 2.452e-05; cls_loss: 0.0495, cls_loss_aux: 0.0539, ptc_loss: 0.0387, aff_loss: 0.7602, kl_loss: 0.0172, seg_loss: 0.0834...
2024-03-28 14:39:59,317 - train_voc_unicam.py - INFO: Iter: 12800; Elasped: 5:27:37; ETA: 3:04:17; LR: 2.393e-05; cls_loss: 0.0486, cls_loss_aux: 0.0510, ptc_loss: 0.0416, aff_loss: 0.7619, kl_loss: 0.0177, seg_loss: 0.0757...
2024-03-28 14:44:27,057 - train_voc_unicam.py - INFO: Iter: 13000; Elasped: 5:32:05; ETA: 2:58:48; LR: 2.333e-05; cls_loss: 0.0432, cls_loss_aux: 0.0463, ptc_loss: 0.0362, aff_loss: 0.7622, kl_loss: 0.0176, seg_loss: 0.0868...
2024-03-28 14:48:53,580 - train_voc_unicam.py - INFO: Iter: 13200; Elasped: 5:36:31; ETA: 2:53:21; LR: 2.273e-05; cls_loss: 0.0455, cls_loss_aux: 0.0497, ptc_loss: 0.0393, aff_loss: 0.7605, kl_loss: 0.0180, seg_loss: 0.0800...
2024-03-28 14:53:23,338 - train_voc_unicam.py - INFO: Iter: 13400; Elasped: 5:41:01; ETA: 2:47:57; LR: 2.212e-05; cls_loss: 0.0443, cls_loss_aux: 0.0425, ptc_loss: 0.0429, aff_loss: 0.7600, kl_loss: 0.0180, seg_loss: 0.0811...
2024-03-28 14:57:50,914 - train_voc_unicam.py - INFO: Iter: 13600; Elasped: 5:45:28; ETA: 2:42:34; LR: 2.152e-05; cls_loss: 0.0373, cls_loss_aux: 0.0360, ptc_loss: 0.0375, aff_loss: 0.7597, kl_loss: 0.0180, seg_loss: 0.0787...
2024-03-28 15:02:18,480 - train_voc_unicam.py - INFO: Iter: 13800; Elasped: 5:49:56; ETA: 2:37:12; LR: 2.091e-05; cls_loss: 0.0357, cls_loss_aux: 0.0347, ptc_loss: 0.0366, aff_loss: 0.7596, kl_loss: 0.0168, seg_loss: 0.0776...
2024-03-28 15:06:46,973 - train_voc_unicam.py - INFO: Iter: 14000; Elasped: 5:54:24; ETA: 2:31:53; LR: 2.031e-05; cls_loss: 0.0365, cls_loss_aux: 0.0358, ptc_loss: 0.0413, aff_loss: 0.7587, kl_loss: 0.0170, seg_loss: 0.0769...
2024-03-28 15:06:46,974 - train_voc_unicam.py - INFO: Validating...
2024-03-28 15:13:33,545 - train_voc_unicam.py - INFO: val cls score: 0.918134
2024-03-28 15:13:33,545 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.660 | 88.876 | 91.529 | 91.318 | 91.178 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 82.550 | 77.783 | 80.531 | 80.522 | 81.783 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 41.839 | 33.794 | 41.096 | 40.424 | 36.589 |
+------------------+--------+---------+---------+--------+----------+
| bird | 83.075 | 81.552 | 83.363 | 81.677 | 82.742 |
+------------------+--------+---------+---------+--------+----------+
| boat | 68.807 | 65.909 | 70.407 | 67.275 | 65.980 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 76.141 | 67.700 | 74.449 | 76.045 | 75.057 |
+------------------+--------+---------+---------+--------+----------+
| bus | 85.458 | 70.091 | 86.623 | 85.008 | 83.282 |
+------------------+--------+---------+---------+--------+----------+
| car | 82.089 | 66.373 | 81.960 | 81.786 | 78.740 |
+------------------+--------+---------+---------+--------+----------+
| cat | 88.159 | 80.451 | 86.382 | 88.593 | 87.721 |
+------------------+--------+---------+---------+--------+----------+
| chair | 44.803 | 37.848 | 42.701 | 44.863 | 36.215 |
+------------------+--------+---------+---------+--------+----------+
| cow | 89.221 | 74.838 | 88.659 | 89.133 | 82.262 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 65.196 | 58.716 | 63.455 | 66.904 | 61.623 |
+------------------+--------+---------+---------+--------+----------+
| dog | 87.556 | 82.623 | 86.857 | 87.455 | 80.576 |
+------------------+--------+---------+---------+--------+----------+
| horse | 86.330 | 71.958 | 85.249 | 85.155 | 79.444 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 77.387 | 70.996 | 75.719 | 77.309 | 72.377 |
+------------------+--------+---------+---------+--------+----------+
| person | 81.435 | 77.818 | 79.883 | 80.770 | 78.936 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 55.795 | 50.812 | 54.430 | 54.875 | 55.851 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 88.758 | 79.145 | 87.595 | 87.555 | 88.039 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 61.175 | 71.014 | 60.255 | 61.212 | 45.355 |
+------------------+--------+---------+---------+--------+----------+
| train | 59.220 | 56.657 | 58.696 | 58.903 | 57.468 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 67.789 | 64.369 | 67.010 | 67.585 | 64.431 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 81.923 | 78.524 | 82.074 | 80.510 | 80.144 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 88.337 | 83.287 | 86.871 | 89.425 | 84.622 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.266 | 0.348 | 0.265 | 0.293 | 0.310 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 74.497 | 68.063 | 73.659 | 74.017 | 70.745 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 15:18:02,211 - train_voc_unicam.py - INFO: Iter: 14200; Elasped: 6:05:40; ETA: 2:29:21; LR: 1.970e-05; cls_loss: 0.0378, cls_loss_aux: 0.0386, ptc_loss: 0.0347, aff_loss: 0.7583, kl_loss: 0.0171, seg_loss: 0.0693...
2024-03-28 15:22:30,478 - train_voc_unicam.py - INFO: Iter: 14400; Elasped: 6:10:08; ETA: 2:23:56; LR: 1.908e-05; cls_loss: 0.0395, cls_loss_aux: 0.0395, ptc_loss: 0.0377, aff_loss: 0.7591, kl_loss: 0.0171, seg_loss: 0.0715...
2024-03-28 15:26:57,091 - train_voc_unicam.py - INFO: Iter: 14600; Elasped: 6:14:35; ETA: 2:18:32; LR: 1.847e-05; cls_loss: 0.0358, cls_loss_aux: 0.0348, ptc_loss: 0.0342, aff_loss: 0.7588, kl_loss: 0.0173, seg_loss: 0.0703...
2024-03-28 15:31:25,435 - train_voc_unicam.py - INFO: Iter: 14800; Elasped: 6:19:03; ETA: 2:13:10; LR: 1.785e-05; cls_loss: 0.0353, cls_loss_aux: 0.0337, ptc_loss: 0.0321, aff_loss: 0.7571, kl_loss: 0.0173, seg_loss: 0.0645...
2024-03-28 15:35:53,933 - train_voc_unicam.py - INFO: Iter: 15000; Elasped: 6:23:31; ETA: 2:07:50; LR: 1.723e-05; cls_loss: 0.0370, cls_loss_aux: 0.0356, ptc_loss: 0.0339, aff_loss: 0.7572, kl_loss: 0.0166, seg_loss: 0.0633...
2024-03-28 15:40:21,915 - train_voc_unicam.py - INFO: Iter: 15200; Elasped: 6:27:59; ETA: 2:02:31; LR: 1.661e-05; cls_loss: 0.0354, cls_loss_aux: 0.0338, ptc_loss: 0.0337, aff_loss: 0.7585, kl_loss: 0.0168, seg_loss: 0.0672...
2024-03-28 15:44:49,495 - train_voc_unicam.py - INFO: Iter: 15400; Elasped: 6:32:27; ETA: 1:57:13; LR: 1.599e-05; cls_loss: 0.0340, cls_loss_aux: 0.0312, ptc_loss: 0.0333, aff_loss: 0.7576, kl_loss: 0.0176, seg_loss: 0.0614...
2024-03-28 15:49:16,939 - train_voc_unicam.py - INFO: Iter: 15600; Elasped: 6:36:54; ETA: 1:51:56; LR: 1.536e-05; cls_loss: 0.0364, cls_loss_aux: 0.0345, ptc_loss: 0.0351, aff_loss: 0.7599, kl_loss: 0.0180, seg_loss: 0.0722...
2024-03-28 15:53:44,984 - train_voc_unicam.py - INFO: Iter: 15800; Elasped: 6:41:22; ETA: 1:46:41; LR: 1.473e-05; cls_loss: 0.0363, cls_loss_aux: 0.0347, ptc_loss: 0.0348, aff_loss: 0.7582, kl_loss: 0.0170, seg_loss: 0.0672...
2024-03-28 15:58:14,110 - train_voc_unicam.py - INFO: Iter: 16000; Elasped: 6:45:52; ETA: 1:41:28; LR: 1.410e-05; cls_loss: 0.0328, cls_loss_aux: 0.0277, ptc_loss: 0.0334, aff_loss: 0.7578, kl_loss: 0.0167, seg_loss: 0.0630...
2024-03-28 15:58:14,111 - train_voc_unicam.py - INFO: Validating...
2024-03-28 16:04:59,482 - train_voc_unicam.py - INFO: val cls score: 0.926895
2024-03-28 16:04:59,483 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.403 | 89.068 | 91.426 | 91.145 | 91.290 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 82.286 | 79.855 | 80.088 | 79.836 | 81.330 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 42.166 | 37.997 | 41.428 | 40.982 | 36.980 |
+------------------+--------+---------+---------+--------+----------+
| bird | 83.115 | 82.287 | 84.357 | 81.998 | 84.972 |
+------------------+--------+---------+---------+--------+----------+
| boat | 62.985 | 66.936 | 65.667 | 61.584 | 62.733 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 74.750 | 61.310 | 74.341 | 74.922 | 75.364 |
+------------------+--------+---------+---------+--------+----------+
| bus | 85.124 | 72.311 | 85.748 | 84.828 | 84.165 |
+------------------+--------+---------+---------+--------+----------+
| car | 82.273 | 70.446 | 81.899 | 82.002 | 78.303 |
+------------------+--------+---------+---------+--------+----------+
| cat | 87.666 | 78.146 | 86.473 | 88.230 | 88.115 |
+------------------+--------+---------+---------+--------+----------+
| chair | 50.050 | 42.094 | 49.107 | 49.971 | 39.350 |
+------------------+--------+---------+---------+--------+----------+
| cow | 86.967 | 68.800 | 85.667 | 87.577 | 83.158 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 60.216 | 59.084 | 59.534 | 62.366 | 59.695 |
+------------------+--------+---------+---------+--------+----------+
| dog | 86.425 | 79.957 | 86.723 | 87.557 | 83.457 |
+------------------+--------+---------+---------+--------+----------+
| horse | 86.874 | 74.041 | 85.553 | 86.323 | 81.494 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 76.268 | 72.536 | 74.951 | 76.308 | 71.914 |
+------------------+--------+---------+---------+--------+----------+
| person | 80.730 | 78.261 | 80.074 | 80.131 | 78.894 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 60.860 | 50.444 | 61.439 | 60.421 | 58.685 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 87.923 | 79.690 | 85.936 | 86.973 | 86.185 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 59.465 | 66.418 | 58.862 | 59.661 | 48.598 |
+------------------+--------+---------+---------+--------+----------+
| train | 60.117 | 60.545 | 59.860 | 59.449 | 58.928 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 66.704 | 66.509 | 66.533 | 66.421 | 62.872 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 82.154 | 79.060 | 82.681 | 80.834 | 81.385 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 87.934 | 83.496 | 86.668 | 89.164 | 83.814 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.262 | 0.324 | 0.252 | 0.286 | 0.280 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 74.017 | 68.416 | 73.603 | 73.747 | 71.261 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 16:09:20,973 - train_voc_unicam.py - INFO: Iter: 16200; Elasped: 6:56:58; ETA: 1:37:48; LR: 1.346e-05; cls_loss: 0.0300, cls_loss_aux: 0.0265, ptc_loss: 0.0344, aff_loss: 0.7591, kl_loss: 0.0161, seg_loss: 0.0570...
2024-03-28 16:13:41,351 - train_voc_unicam.py - INFO: Iter: 16400; Elasped: 7:01:19; ETA: 1:32:29; LR: 1.282e-05; cls_loss: 0.0275, cls_loss_aux: 0.0236, ptc_loss: 0.0325, aff_loss: 0.7584, kl_loss: 0.0163, seg_loss: 0.0589...
2024-03-28 16:18:01,883 - train_voc_unicam.py - INFO: Iter: 16600; Elasped: 7:05:39; ETA: 1:27:10; LR: 1.218e-05; cls_loss: 0.0321, cls_loss_aux: 0.0286, ptc_loss: 0.0355, aff_loss: 0.7577, kl_loss: 0.0172, seg_loss: 0.0635...
2024-03-28 16:22:22,371 - train_voc_unicam.py - INFO: Iter: 16800; Elasped: 7:10:00; ETA: 1:21:54; LR: 1.153e-05; cls_loss: 0.0306, cls_loss_aux: 0.0254, ptc_loss: 0.0325, aff_loss: 0.7572, kl_loss: 0.0174, seg_loss: 0.0655...
2024-03-28 16:26:43,457 - train_voc_unicam.py - INFO: Iter: 17000; Elasped: 7:14:21; ETA: 1:16:39; LR: 1.088e-05; cls_loss: 0.0362, cls_loss_aux: 0.0325, ptc_loss: 0.0358, aff_loss: 0.7565, kl_loss: 0.0168, seg_loss: 0.0679...
2024-03-28 16:31:03,985 - train_voc_unicam.py - INFO: Iter: 17200; Elasped: 7:18:41; ETA: 1:11:24; LR: 1.023e-05; cls_loss: 0.0365, cls_loss_aux: 0.0322, ptc_loss: 0.0322, aff_loss: 0.7575, kl_loss: 0.0167, seg_loss: 0.0714...
2024-03-28 16:35:24,161 - train_voc_unicam.py - INFO: Iter: 17400; Elasped: 7:23:02; ETA: 1:06:12; LR: 9.569e-06; cls_loss: 0.0265, cls_loss_aux: 0.0228, ptc_loss: 0.0354, aff_loss: 0.7580, kl_loss: 0.0178, seg_loss: 0.0594...
2024-03-28 16:39:43,556 - train_voc_unicam.py - INFO: Iter: 17600; Elasped: 7:27:21; ETA: 1:01:00; LR: 8.904e-06; cls_loss: 0.0307, cls_loss_aux: 0.0257, ptc_loss: 0.0328, aff_loss: 0.7561, kl_loss: 0.0172, seg_loss: 0.0613...
2024-03-28 16:44:04,335 - train_voc_unicam.py - INFO: Iter: 17800; Elasped: 7:31:42; ETA: 0:55:49; LR: 8.233e-06; cls_loss: 0.0338, cls_loss_aux: 0.0297, ptc_loss: 0.0337, aff_loss: 0.7568, kl_loss: 0.0169, seg_loss: 0.0643...
2024-03-28 16:48:24,626 - train_voc_unicam.py - INFO: Iter: 18000; Elasped: 7:36:02; ETA: 0:50:40; LR: 7.557e-06; cls_loss: 0.0346, cls_loss_aux: 0.0295, ptc_loss: 0.0362, aff_loss: 0.7556, kl_loss: 0.0166, seg_loss: 0.0630...
2024-03-28 16:48:24,626 - train_voc_unicam.py - INFO: Validating...
2024-03-28 16:55:08,525 - train_voc_unicam.py - INFO: val cls score: 0.924692
2024-03-28 16:55:08,525 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.805 | 88.332 | 91.666 | 91.673 | 91.442 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 83.792 | 79.348 | 82.394 | 82.517 | 81.405 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 40.795 | 34.182 | 39.736 | 39.778 | 37.461 |
+------------------+--------+---------+---------+--------+----------+
| bird | 85.447 | 84.230 | 85.630 | 84.182 | 86.188 |
+------------------+--------+---------+---------+--------+----------+
| boat | 69.944 | 68.066 | 71.586 | 69.065 | 69.570 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 75.275 | 60.300 | 74.416 | 75.249 | 76.100 |
+------------------+--------+---------+---------+--------+----------+
| bus | 86.302 | 72.140 | 86.936 | 86.383 | 85.869 |
+------------------+--------+---------+---------+--------+----------+
| car | 80.213 | 62.337 | 79.153 | 80.562 | 77.646 |
+------------------+--------+---------+---------+--------+----------+
| cat | 86.681 | 76.649 | 84.839 | 87.449 | 87.116 |
+------------------+--------+---------+---------+--------+----------+
| chair | 49.097 | 38.910 | 47.696 | 48.990 | 38.762 |
+------------------+--------+---------+---------+--------+----------+
| cow | 85.611 | 63.052 | 84.521 | 86.482 | 84.505 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 55.341 | 55.189 | 54.658 | 58.128 | 52.917 |
+------------------+--------+---------+---------+--------+----------+
| dog | 85.752 | 78.160 | 85.743 | 86.758 | 83.758 |
+------------------+--------+---------+---------+--------+----------+
| horse | 85.409 | 69.264 | 83.964 | 85.495 | 81.666 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 75.335 | 69.810 | 74.371 | 75.718 | 72.588 |
+------------------+--------+---------+---------+--------+----------+
| person | 80.763 | 78.393 | 79.494 | 80.151 | 78.489 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 60.517 | 45.745 | 58.748 | 60.216 | 59.291 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 88.109 | 81.678 | 85.402 | 87.731 | 85.974 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 61.163 | 67.901 | 60.810 | 61.549 | 48.366 |
+------------------+--------+---------+---------+--------+----------+
| train | 65.625 | 63.527 | 64.941 | 65.393 | 63.736 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 67.948 | 65.622 | 67.813 | 68.370 | 63.833 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 84.273 | 78.117 | 84.555 | 83.003 | 83.198 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 85.857 | 82.056 | 84.491 | 87.355 | 82.618 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.227 | 0.359 | 0.224 | 0.250 | 0.253 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 74.330 | 66.802 | 73.548 | 74.373 | 71.747 |
+------------------+--------+---------+---------+--------+----------+
2024-03-28 16:59:28,981 - train_voc_unicam.py - INFO: Iter: 18200; Elasped: 7:47:06; ETA: 0:46:11; LR: 6.874e-06; cls_loss: 0.0316, cls_loss_aux: 0.0276, ptc_loss: 0.0320, aff_loss: 0.7560, kl_loss: 0.0169, seg_loss: 0.0613...
2024-03-28 17:03:49,414 - train_voc_unicam.py - INFO: Iter: 18400; Elasped: 7:51:27; ETA: 0:40:59; LR: 6.183e-06; cls_loss: 0.0313, cls_loss_aux: 0.0264, ptc_loss: 0.0317, aff_loss: 0.7561, kl_loss: 0.0167, seg_loss: 0.0588...
2024-03-28 17:08:11,698 - train_voc_unicam.py - INFO: Iter: 18600; Elasped: 7:55:49; ETA: 0:35:48; LR: 5.483e-06; cls_loss: 0.0270, cls_loss_aux: 0.0235, ptc_loss: 0.0316, aff_loss: 0.7575, kl_loss: 0.0160, seg_loss: 0.0542...
2024-03-28 17:12:33,269 - train_voc_unicam.py - INFO: Iter: 18800; Elasped: 8:00:11; ETA: 0:30:39; LR: 4.773e-06; cls_loss: 0.0277, cls_loss_aux: 0.0222, ptc_loss: 0.0320, aff_loss: 0.7553, kl_loss: 0.0161, seg_loss: 0.0597...
2024-03-28 17:16:53,703 - train_voc_unicam.py - INFO: Iter: 19000; Elasped: 8:04:31; ETA: 0:25:30; LR: 4.051e-06; cls_loss: 0.0263, cls_loss_aux: 0.0219, ptc_loss: 0.0296, aff_loss: 0.7570, kl_loss: 0.0166, seg_loss: 0.0655...
2024-03-28 17:21:14,019 - train_voc_unicam.py - INFO: Iter: 19200; Elasped: 8:08:52; ETA: 0:20:22; LR: 3.315e-06; cls_loss: 0.0237, cls_loss_aux: 0.0182, ptc_loss: 0.0288, aff_loss: 0.7567, kl_loss: 0.0157, seg_loss: 0.0555...
2024-03-28 17:25:33,773 - train_voc_unicam.py - INFO: Iter: 19400; Elasped: 8:13:11; ETA: 0:15:15; LR: 2.560e-06; cls_loss: 0.0288, cls_loss_aux: 0.0228, ptc_loss: 0.0317, aff_loss: 0.7567, kl_loss: 0.0170, seg_loss: 0.0585...
2024-03-28 17:29:53,546 - train_voc_unicam.py - INFO: Iter: 19600; Elasped: 8:17:31; ETA: 0:10:09; LR: 1.779e-06; cls_loss: 0.0278, cls_loss_aux: 0.0215, ptc_loss: 0.0314, aff_loss: 0.7559, kl_loss: 0.0167, seg_loss: 0.0616...
2024-03-28 17:34:12,639 - train_voc_unicam.py - INFO: Iter: 19800; Elasped: 8:21:50; ETA: 0:05:04; LR: 9.552e-07; cls_loss: 0.0270, cls_loss_aux: 0.0214, ptc_loss: 0.0337, aff_loss: 0.7569, kl_loss: 0.0170, seg_loss: 0.0639...
2024-03-28 17:38:31,710 - train_voc_unicam.py - INFO: Iter: 20000; Elasped: 8:26:09; ETA: 0:00:00; LR: 8.077e-09; cls_loss: 0.0287, cls_loss_aux: 0.0230, ptc_loss: 0.0323, aff_loss: 0.7554, kl_loss: 0.0161, seg_loss: 0.0639...
2024-03-28 17:38:31,710 - train_voc_unicam.py - INFO: Validating...
2024-03-28 17:45:14,954 - train_voc_unicam.py - INFO: val cls score: 0.927731
2024-03-28 17:45:14,955 - train_voc_unicam.py - INFO:
+------------------+--------+---------+---------+--------+----------+
| Class | CAM | aux_CAM | aff_Map | dr_Map | Seg_Pred |
+==================+========+=========+=========+========+==========+
| _background_ | 91.408 | 88.394 | 91.294 | 91.259 | 91.189 |
+------------------+--------+---------+---------+--------+----------+
| aeroplane | 82.737 | 79.921 | 81.259 | 80.770 | 82.598 |
+------------------+--------+---------+---------+--------+----------+
| bicycle | 40.076 | 33.934 | 39.140 | 39.046 | 37.312 |
+------------------+--------+---------+---------+--------+----------+
| bird | 85.810 | 84.715 | 85.735 | 84.604 | 86.475 |
+------------------+--------+---------+---------+--------+----------+
| boat | 66.208 | 67.372 | 68.675 | 64.807 | 65.828 |
+------------------+--------+---------+---------+--------+----------+
| bottle | 75.872 | 63.827 | 75.393 | 75.809 | 77.120 |
+------------------+--------+---------+---------+--------+----------+
| bus | 86.574 | 73.147 | 87.221 | 86.505 | 85.412 |
+------------------+--------+---------+---------+--------+----------+
| car | 81.131 | 62.934 | 79.771 | 81.529 | 79.514 |
+------------------+--------+---------+---------+--------+----------+
| cat | 84.788 | 74.579 | 83.041 | 85.824 | 86.753 |
+------------------+--------+---------+---------+--------+----------+
| chair | 49.280 | 39.518 | 48.202 | 49.022 | 38.976 |
+------------------+--------+---------+---------+--------+----------+
| cow | 84.341 | 65.991 | 82.675 | 85.287 | 84.959 |
+------------------+--------+---------+---------+--------+----------+
| diningtable | 59.627 | 57.202 | 58.471 | 61.642 | 57.788 |
+------------------+--------+---------+---------+--------+----------+
| dog | 83.219 | 73.794 | 83.095 | 84.820 | 82.764 |
+------------------+--------+---------+---------+--------+----------+
| horse | 84.558 | 67.732 | 83.133 | 84.832 | 82.200 |
+------------------+--------+---------+---------+--------+----------+
| motorbike | 75.670 | 71.903 | 74.309 | 75.830 | 73.495 |
+------------------+--------+---------+---------+--------+----------+
| person | 80.011 | 77.485 | 78.660 | 79.563 | 78.542 |
+------------------+--------+---------+---------+--------+----------+
| pottedplant | 61.637 | 50.988 | 60.162 | 61.621 | 59.880 |
+------------------+--------+---------+---------+--------+----------+
| sheep | 87.564 | 81.120 | 84.883 | 87.471 | 86.307 |
+------------------+--------+---------+---------+--------+----------+
| sofa | 63.603 | 69.247 | 62.978 | 64.095 | 50.176 |
+------------------+--------+---------+---------+--------+----------+
| train | 63.589 | 61.497 | 63.005 | 62.913 | 61.942 |
+------------------+--------+---------+---------+--------+----------+
| tvmonitor | 66.083 | 67.176 | 66.833 | 65.905 | 61.412 |
+------------------+--------+---------+---------+--------+----------+
| m-Precision | 83.355 | 79.026 | 83.686 | 82.133 | 81.865 |
+------------------+--------+---------+---------+--------+----------+
| m-Recall | 86.688 | 81.995 | 85.227 | 88.069 | 84.393 |
+------------------+--------+---------+---------+--------+----------+
| m-ConfutionRatio | 0.247 | 0.339 | 0.242 | 0.270 | 0.279 |
+------------------+--------+---------+---------+--------+----------+
| m-IoU | 73.990 | 67.261 | 73.235 | 73.960 | 71.935 |
+------------------+--------+---------+---------+--------+----------+