diff --git a/docs/source/guide/ml_tutorials.html b/docs/source/guide/ml_tutorials.html index a3dd1c1445e0..ab8b112b2181 100644 --- a/docs/source/guide/ml_tutorials.html +++ b/docs/source/guide/ml_tutorials.html @@ -1,5 +1,35 @@ --- cards: +- categories: + - Computer Vision + - Image Annotation + - Object Detection + - Grounding DINO + hide_frontmatter_title: true + hide_menu: true + image: /tutorials/grounding-dino.png + meta_description: Label Studio tutorial for using Grounding DINO for zero-shot object + detection in images + meta_title: Image segmentation in Label Studio using a Grounding DINO backend + order: 15 + tier: all + title: Zero-shot object detection and image segmentation with Grounding DINO + type: guide + url: /tutorials/grounding_dino.html +- categories: + - Computer Vision + - Video Annotation + - Object Detection + - Segment Anything Model + hide_frontmatter_title: true + hide_menu: true + image: /tutorials/sam2-video.png + meta_title: Using SAM2 with Label Studio for Video Annotation + order: 15 + tier: all + title: SAM2 with Videos + type: guide + url: /tutorials/segment_anything_2_video.html - categories: - Natural Language Processing - Text Classification @@ -19,104 +49,94 @@ - categories: - Computer Vision - Optical Character Recognition - - EasyOCR + - Tesseract hide_frontmatter_title: true hide_menu: true - image: /tutorials/easyocr.png - meta_description: The EasyOCR model connection integrates the capabilities of EasyOCR - with Label Studio to assist in machine learning labeling tasks involving Optical - Character Recognition (OCR). - meta_title: EasyOCR model connection for transcribing text in images - order: 40 + image: /tutorials/tesseract.png + meta_description: Tutorial for how to use Label Studio and Tesseract to assist with + your OCR projects + meta_title: Interactive bounding boxes OCR in Label Studio with a Tesseract backend + order: 55 tier: all - title: Transcribe text from images with EasyOCR + title: Interactive bounding boxes OCR with Tesseract type: guide - url: /tutorials/easyocr.html + url: /tutorials/tesseract.html - categories: - - Natural Language Processing - - Named Entity Recognition - - Flair + - Generative AI + - Retrieval Augmented Generation + - Google + - OpenAI + - Langchain hide_frontmatter_title: true hide_menu: true - image: /tutorials/flair.png - meta_description: Tutorial on how to use Label Studio and Flair for faster NER labeling - meta_title: Use Flair with Label Studio - order: 75 + image: /tutorials/langchain.png + meta_description: Use Langchain, OpenAI, and Google to generate responses based + on Google search results. + meta_title: RAG with a Langchain search agent + order: 45 tier: all - title: NER labeling with Flair + title: RAG with a Langchain search agent type: guide - url: /tutorials/flair.html + url: /tutorials/langchain_search_agent.html - categories: - - Natural Language Processing - - Named Entity Recognition - - GLiNER - - BERT - - Hugging Face + - Audio/Speech Processing + - Automatic Speech Recognition + - NeMo + - NVidia hide_frontmatter_title: true hide_menu: true - image: /tutorials/gliner.png - meta_description: Tutorial on how to use GLiNER with your Label Studio project to - complete NER tasks - meta_title: Use GLiNER for NER annotation - order: 37 + image: /tutorials/nvidia.png + meta_description: Tutorial on how to use set up Nvidia NeMo to use for ASR tasks + in Label Studio + meta_title: Automatic Speech Recognition with NeMo + order: 60 tier: all - title: Use GLiNER for NER annotation + title: Automatic Speech Recognition with NVidia NeMo type: guide - url: /tutorials/gliner.html + url: /tutorials/nemo_asr.html - categories: - - Computer Vision - - Image Annotation - - Object Detection - - Grounding DINO + - Natural Language Processing + - Named Entity Recognition + - Interactive matching hide_frontmatter_title: true hide_menu: true - image: /tutorials/grounding-dino.png - meta_description: Label Studio tutorial for using Grounding DINO for zero-shot object - detection in images - meta_title: Image segmentation in Label Studio using a Grounding DINO backend - order: 15 + image: /tutorials/interactive-substring-matching.png + meta_description: Use the interactive substring matching model for labeling NER + tasks in Label Studio + meta_title: Interactive substring matching for NER tasks + order: 30 tier: all - title: Zero-shot object detection and image segmentation with Grounding DINO + title: Interactive substring matching for NER tasks type: guide - url: /tutorials/grounding_dino.html + url: /tutorials/interactive_substring_matching.html - categories: - Computer Vision - - Image Annotation - - Object Detection - - Zero-shot Image Segmentation - - Grounding DINO - - Segment Anything Model + - Large Language Model + - WatsonX hide_frontmatter_title: true hide_menu: true - image: /tutorials/grounding-sam.png - meta_description: Label Studio tutorial for using Grounding DINO and SAM for zero-shot - object detection in images - meta_title: Image segmentation in Label Studio using a Grounding DINO backend and - SAM + image: /tutorials/watsonx.png + meta_title: Integrate WatsonX with Label Studio order: 15 tier: all - title: Zero-shot object detection and image segmentation with Grounding DINO and - SAM + title: Integrate WatsonX with Label Studio type: guide - url: /tutorials/grounding_sam.html + url: /tutorials/watsonx_llm.html - categories: - - Generative AI - - Large Language Model - - Text Generation - - Hugging Face + - Natural Language Processing + - Named Entity Recognition + - SpaCy hide_frontmatter_title: true hide_menu: true - image: /tutorials/hf-llm.png - meta_description: This tutorial explains how to run Hugging Face Large Language - model backend in Label Studio. Hugging Face Large Language Model Backend is a - machine learning backend designed to work with Label Studio, providing a custom - model for text generation. - meta_title: Label Studio tutorial to run Hugging Face Large Language Model backend - order: 20 + image: /tutorials/spacy.png + meta_description: Tutorial on how to use Label Studio and spaCy for faster NER and + POS labeling + meta_title: Use spaCy models with Label Studio + order: 70 tier: all - title: Hugging Face Large Language Model (LLM) + title: spaCy models for NER type: guide - url: /tutorials/huggingface_llm.html + url: /tutorials/spacy.html - categories: - Natural Language Processing - Named Entity Recognition @@ -134,87 +154,71 @@ url: /tutorials/huggingface_ner.html - categories: - Natural Language Processing - - Named Entity Recognition - - Interactive matching + - Text Classification + - Scikit-learn hide_frontmatter_title: true hide_menu: true - image: /tutorials/interactive-substring-matching.png - meta_description: Use the interactive substring matching model for labeling NER - tasks in Label Studio - meta_title: Interactive substring matching for NER tasks - order: 30 + image: /tutorials/scikit-learn.png + meta_description: Tutorial on how to use an example ML backend for Label Studio + with Scikit-learn logistic regression + meta_title: Sklearn Text Classifier model for Label Studio + order: 50 tier: all - title: Interactive substring matching for NER tasks + title: Sklearn Text Classifier model type: guide - url: /tutorials/interactive_substring_matching.html + url: /tutorials/sklearn_text_classifier.html - categories: - - Generative AI - - Retrieval Augmented Generation - - Google - - OpenAI - - Langchain + - Computer Vision + - Optical Character Recognition + - EasyOCR hide_frontmatter_title: true hide_menu: true - image: /tutorials/langchain.png - meta_description: Use Langchain, OpenAI, and Google to generate responses based - on Google search results. - meta_title: RAG with a Langchain search agent - order: 45 + image: /tutorials/easyocr.png + meta_description: The EasyOCR model connection integrates the capabilities of EasyOCR + with Label Studio to assist in machine learning labeling tasks involving Optical + Character Recognition (OCR). + meta_title: EasyOCR model connection for transcribing text in images + order: 40 tier: all - title: RAG with a Langchain search agent + title: Transcribe text from images with EasyOCR type: guide - url: /tutorials/langchain_search_agent.html + url: /tutorials/easyocr.html - categories: - Generative AI - Large Language Model - - OpenAI - - Azure - - Ollama - - ChatGPT + - Text Generation + - Hugging Face hide_frontmatter_title: true hide_menu: true - image: /tutorials/llm-interactive.png - meta_description: Label Studio tutorial for interactive LLM labeling with OpenAI, - Azure, or Ollama - meta_title: Interactive LLM labeling with OpenAI, Azure, or Ollama - order: 5 + image: /tutorials/hf-llm.png + meta_description: This tutorial explains how to run Hugging Face Large Language + model backend in Label Studio. Hugging Face Large Language Model Backend is a + machine learning backend designed to work with Label Studio, providing a custom + model for text generation. + meta_title: Label Studio tutorial to run Hugging Face Large Language Model backend + order: 20 tier: all - title: Interactive LLM labeling with GPT + title: Hugging Face Large Language Model (LLM) type: guide - url: /tutorials/llm_interactive.html + url: /tutorials/huggingface_llm.html - categories: - Computer Vision - Object Detection - Image Annotation - - OpenMMLab - - MMDetection - hide_frontmatter_title: true - hide_menu: true - image: /tutorials/openmmlab.png - meta_description: This is a tutorial on how to use the example MMDetection model - backend with Label Studio for image segmentation tasks. - meta_title: Object detection in images with Label Studio and MMDetection - order: 65 - tier: all - title: Object detection with bounding boxes using MMDetection - type: guide - url: /tutorials/mmdetection-3.html -- categories: - - Audio/Speech Processing - - Automatic Speech Recognition - - NeMo - - NVidia + - Segment Anything Model + - Facebook + - ONNX hide_frontmatter_title: true hide_menu: true - image: /tutorials/nvidia.png - meta_description: Tutorial on how to use set up Nvidia NeMo to use for ASR tasks - in Label Studio - meta_title: Automatic Speech Recognition with NeMo - order: 60 + image: /tutorials/segment-anything.png + meta_description: Label Studio tutorial for labeling images with MobileSAM or ONNX + SAM. + meta_title: Interactive annotation in Label Studio with Segment Anything Model (SAM) + order: 10 tier: all - title: Automatic Speech Recognition with NVidia NeMo + title: Interactive annotation with Segment Anything Model type: guide - url: /tutorials/nemo_asr.html + url: /tutorials/segment_anything_model.html - categories: - Computer Vision - Image Annotation @@ -229,112 +233,92 @@ title: SAM2 with Images type: guide url: /tutorials/segment_anything_2_image.html -- categories: - - Computer Vision - - Video Annotation - - Object Detection - - Segment Anything Model - hide_frontmatter_title: true - hide_menu: true - image: /tutorials/sam2-video.png - meta_title: Using SAM2 with Label Studio for Video Annotation - order: 15 - tier: all - title: SAM2 with Videos - type: guide - url: /tutorials/segment_anything_2_video.html -- categories: - - Computer Vision - - Object Detection - - Image Annotation - - Segment Anything Model - - Facebook - - ONNX - hide_frontmatter_title: true - hide_menu: true - image: /tutorials/segment-anything.png - meta_description: Label Studio tutorial for labeling images with MobileSAM or ONNX - SAM. - meta_title: Interactive annotation in Label Studio with Segment Anything Model (SAM) - order: 10 - tier: all - title: Interactive annotation with Segment Anything Model - type: guide - url: /tutorials/segment_anything_model.html - categories: - Natural Language Processing - - Text Classification - - Scikit-learn + - Named Entity Recognition + - GLiNER + - BERT + - Hugging Face hide_frontmatter_title: true hide_menu: true - image: /tutorials/scikit-learn.png - meta_description: Tutorial on how to use an example ML backend for Label Studio - with Scikit-learn logistic regression - meta_title: Sklearn Text Classifier model for Label Studio - order: 50 + image: /tutorials/gliner.png + meta_description: Tutorial on how to use GLiNER with your Label Studio project to + complete NER tasks + meta_title: Use GLiNER for NER annotation + order: 37 tier: all - title: Sklearn Text Classifier model + title: Use GLiNER for NER annotation type: guide - url: /tutorials/sklearn_text_classifier.html + url: /tutorials/gliner.html - categories: - Natural Language Processing - Named Entity Recognition - - SpaCy + - Flair hide_frontmatter_title: true hide_menu: true - image: /tutorials/spacy.png - meta_description: Tutorial on how to use Label Studio and spaCy for faster NER and - POS labeling - meta_title: Use spaCy models with Label Studio - order: 70 + image: /tutorials/flair.png + meta_description: Tutorial on how to use Label Studio and Flair for faster NER labeling + meta_title: Use Flair with Label Studio + order: 75 tier: all - title: spaCy models for NER + title: NER labeling with Flair type: guide - url: /tutorials/spacy.html + url: /tutorials/flair.html - categories: - - Computer Vision - - Optical Character Recognition - - Tesseract + - Generative AI + - Large Language Model + - OpenAI + - Azure + - Ollama + - ChatGPT hide_frontmatter_title: true hide_menu: true - image: /tutorials/tesseract.png - meta_description: Tutorial for how to use Label Studio and Tesseract to assist with - your OCR projects - meta_title: Interactive bounding boxes OCR in Label Studio with a Tesseract backend - order: 55 + image: /tutorials/llm-interactive.png + meta_description: Label Studio tutorial for interactive LLM labeling with OpenAI, + Azure, or Ollama + meta_title: Interactive LLM labeling with OpenAI, Azure, or Ollama + order: 5 tier: all - title: Interactive bounding boxes OCR with Tesseract + title: Interactive LLM labeling with GPT type: guide - url: /tutorials/tesseract.html + url: /tutorials/llm_interactive.html - categories: - Computer Vision - - Large Language Model - - WatsonX + - Object Detection + - Image Annotation + - OpenMMLab + - MMDetection hide_frontmatter_title: true hide_menu: true - image: /tutorials/watsonx.png - meta_title: Integrate WatsonX with Label Studio - order: 15 + image: /tutorials/openmmlab.png + meta_description: This is a tutorial on how to use the example MMDetection model + backend with Label Studio for image segmentation tasks. + meta_title: Object detection in images with Label Studio and MMDetection + order: 65 tier: all - title: Integrate WatsonX with Label Studio + title: Object detection with bounding boxes using MMDetection type: guide - url: /tutorials/watsonx_llm.html + url: /tutorials/mmdetection-3.html - categories: - Computer Vision + - Image Annotation - Object Detection - - Image Segmentation - - YOLO + - Zero-shot Image Segmentation + - Grounding DINO + - Segment Anything Model hide_frontmatter_title: true hide_menu: true - image: /tutorials/yolo.png - meta_description: Tutorial on how to use an example ML backend for Label Studio - with YOLO - meta_title: YOLO ML Backend for Label Studio - order: 50 + image: /tutorials/grounding-sam.png + meta_description: Label Studio tutorial for using Grounding DINO and SAM for zero-shot + object detection in images + meta_title: Image segmentation in Label Studio using a Grounding DINO backend and + SAM + order: 15 tier: all - title: YOLO ML Backend for Label Studio + title: Zero-shot object detection and image segmentation with Grounding DINO and + SAM type: guide - url: /tutorials/yolo.html + url: /tutorials/grounding_sam.html layout: templates meta_description: Tutorial documentation for setting up a machine learning model with predictions using PyTorch, GPT2, Sci-kit learn, and other popular frameworks. diff --git a/docs/source/tutorials/bert_classifier.md b/docs/source/tutorials/bert_classifier.md index 97594b68a1a1..06872e582a74 100644 --- a/docs/source/tutorials/bert_classifier.md +++ b/docs/source/tutorials/bert_classifier.md @@ -15,6 +15,10 @@ categories: image: "/tutorials/bert.png" --- + + # BERT-based text classification The NewModel is a BERT-based text classification model that is designed to work with Label Studio. This model uses the Hugging Face Transformers library to fine-tune a BERT model for text classification. The model is trained on the labeled data from Label Studio and then used to make predictions on new data. With this model connected to Label Studio, you can: diff --git a/docs/source/tutorials/easyocr.md b/docs/source/tutorials/easyocr.md index 82ac3b4fccbf..a32058cbbbda 100644 --- a/docs/source/tutorials/easyocr.md +++ b/docs/source/tutorials/easyocr.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/easyocr.png" --- + + # EasyOCR model connection The [EasyOCR](https://github.com/JaidedAI/EasyOCR) model connection is a powerful tool that integrates the capabilities of EasyOCR with Label Studio. It is designed to assist in machine learning labeling tasks, specifically those involving Optical Character Recognition (OCR). diff --git a/docs/source/tutorials/flair.md b/docs/source/tutorials/flair.md index 6008612eb4df..899cb8f82423 100644 --- a/docs/source/tutorials/flair.md +++ b/docs/source/tutorials/flair.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/flair.png" --- + + # Flair NER example This example demonstrates how to use Flair NER model with Label Studio. diff --git a/docs/source/tutorials/gliner.md b/docs/source/tutorials/gliner.md index e2c8e28dbc16..31048adba9d6 100644 --- a/docs/source/tutorials/gliner.md +++ b/docs/source/tutorials/gliner.md @@ -16,6 +16,10 @@ categories: image: "/tutorials/gliner.png" --- + + # Use GLiNER for NER annotation The GLiNER model is a BERT family model for generalist NER. We download the model from HuggingFace, but the original diff --git a/docs/source/tutorials/grounding_dino.md b/docs/source/tutorials/grounding_dino.md index e27776f26f90..5b91a0a4ccee 100644 --- a/docs/source/tutorials/grounding_dino.md +++ b/docs/source/tutorials/grounding_dino.md @@ -15,6 +15,10 @@ categories: image: "/tutorials/grounding-dino.png" --- + + https://github.com/HumanSignal/label-studio-ml-backend/assets/106922533/d1d2f233-d7c0-40ac-ba6f-368c3c01fd36 diff --git a/docs/source/tutorials/grounding_sam.md b/docs/source/tutorials/grounding_sam.md index 37bb01383a52..002ddfd5b102 100644 --- a/docs/source/tutorials/grounding_sam.md +++ b/docs/source/tutorials/grounding_sam.md @@ -125,4 +125,4 @@ https://github.com/HumanSignal/label-studio-ml-backend/assets/106922533/79b788e3 Adjust `BOX_THRESHOLD` and `TEXT_THRESHOLD` values in the Dockerfile to a number between 0 to 1 if experimenting. Defaults are set in `dino.py`. For more information about these values, [click here](https://github.com/IDEA-Research/GroundingDINO#star-explanationstips-for-grounding-dino-inputs-and-outputs). -If you want to use SAM models saved from either directories, you can use the `MOBILESAM_CHECKPOINT` and `SAM_CHECKPOINT` as shown in the Dockerfile. \ No newline at end of file +If you want to use SAM models saved from either directories, you can use the `MOBILESAM_CHECKPOINT` and `SAM_CHECKPOINT` as shown in the Dockerfile. diff --git a/docs/source/tutorials/huggingface_llm.md b/docs/source/tutorials/huggingface_llm.md index 6daa9ae78982..8f0f177471c2 100644 --- a/docs/source/tutorials/huggingface_llm.md +++ b/docs/source/tutorials/huggingface_llm.md @@ -15,6 +15,10 @@ categories: image: "/tutorials/hf-llm.png" --- + + # Hugging Face Large Language Model backend This machine learning backend is designed to work with Label Studio, providing a custom model for text generation. The model is based on the Hugging Face's transformers library and uses a pre-trained model. diff --git a/docs/source/tutorials/huggingface_ner.md b/docs/source/tutorials/huggingface_ner.md index ff68d2a13114..00cc99eb968a 100644 --- a/docs/source/tutorials/huggingface_ner.md +++ b/docs/source/tutorials/huggingface_ner.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/hf-ner.png" --- + + # Hugging Face NER model with Label Studio This project uses a custom machine learning backend model for Named Entity Recognition (NER) with Hugging Face's transformers and Label Studio. diff --git a/docs/source/tutorials/interactive_substring_matching.md b/docs/source/tutorials/interactive_substring_matching.md index c271b10730ba..6ec043868766 100644 --- a/docs/source/tutorials/interactive_substring_matching.md +++ b/docs/source/tutorials/interactive_substring_matching.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/interactive-substring-matching.png" --- + + # Interactive substring matching The Machine Learning (ML) backend is designed to enhance the efficiency of auto-labeling in Named Entity Recognition (NER) tasks. It achieves this by selecting a keyword and automatically matching the same keyword in the provided text. diff --git a/docs/source/tutorials/langchain_search_agent.md b/docs/source/tutorials/langchain_search_agent.md index bfb22480188f..d67cfd453546 100644 --- a/docs/source/tutorials/langchain_search_agent.md +++ b/docs/source/tutorials/langchain_search_agent.md @@ -16,6 +16,12 @@ categories: image: "/tutorials/langchain.png" --- + + + + # Langchain search agent This example demonstrates how to use Label Studio with a custom Machine Learning backend. diff --git a/docs/source/tutorials/llm_interactive.md b/docs/source/tutorials/llm_interactive.md index d7234a59d7ea..f2f0a16dbd42 100644 --- a/docs/source/tutorials/llm_interactive.md +++ b/docs/source/tutorials/llm_interactive.md @@ -17,6 +17,10 @@ categories: image: "/tutorials/llm-interactive.png" --- + + # Interactive LLM labeling This example server connects Label Studio to [OpenAI](https://platform.openai.com/), [Ollama](https://ollama.com/), diff --git a/docs/source/tutorials/mmdetection-3.md b/docs/source/tutorials/mmdetection-3.md index a8a95043be65..6f680e84c085 100644 --- a/docs/source/tutorials/mmdetection-3.md +++ b/docs/source/tutorials/mmdetection-3.md @@ -16,6 +16,10 @@ categories: image: "/tutorials/openmmlab.png" --- + + # Object detection with bounding boxes using MMDetection https://mmdetection.readthedocs.io/en/latest/ @@ -23,7 +27,7 @@ https://mmdetection.readthedocs.io/en/latest/ This example demonstrates how to use the MMDetection model with Label Studio to annotate images with bounding boxes. The model is based on the YOLOv3 architecture with a MobileNetV2 backbone and trained on the COCO dataset. -![screenshot.png](screenshot.png) +![screenshot.png](/tutorials/screenshot.png) ## Quick usage @@ -160,4 +164,4 @@ gunicorn --preload --bind :9090 --workers 1 --threads 1 --timeout 0 _wsgi:app ``` * Use this guide to find out your access token: https://labelstud.io/guide/api.html -* You can use and increased value of `SCORE_THRESHOLD` parameter when you see a lot of unwanted detections or lower its value if you don't see any detections. \ No newline at end of file +* You can use and increased value of `SCORE_THRESHOLD` parameter when you see a lot of unwanted detections or lower its value if you don't see any detections. diff --git a/docs/source/tutorials/nemo_asr.md b/docs/source/tutorials/nemo_asr.md index b88a410a7cdc..413375d1e390 100644 --- a/docs/source/tutorials/nemo_asr.md +++ b/docs/source/tutorials/nemo_asr.md @@ -15,6 +15,10 @@ categories: image: "/tutorials/nvidia.png" --- + + # ASR with NeMo This example demonstrates how to use the [NeMo](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/README.md) to perform ASR (Automatic Speech Recognition) in Label Studio. diff --git a/docs/source/tutorials/segment_anything_2_image.md b/docs/source/tutorials/segment_anything_2_image.md index 71e82cf6ba19..75d2368de652 100644 --- a/docs/source/tutorials/segment_anything_2_image.md +++ b/docs/source/tutorials/segment_anything_2_image.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/sam2-images.png" --- + + # Using SAM2 with Label Studio for Image Annotation Segment Anything 2, or SAM 2, is a model released by Meta in July 2024. An update to the original Segment Anything Model, diff --git a/docs/source/tutorials/segment_anything_2_video.md b/docs/source/tutorials/segment_anything_2_video.md index 1d081f3c1148..7561c8f7b6c2 100644 --- a/docs/source/tutorials/segment_anything_2_video.md +++ b/docs/source/tutorials/segment_anything_2_video.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/sam2-video.png" --- + + # Using SAM2 with Label Studio for Video Annotation This guide describes the simplest way to start using **SegmentAnything 2** with Label Studio. @@ -21,7 +25,7 @@ This guide describes the simplest way to start using **SegmentAnything 2** with This repository is specifically for working with object tracking in videos. For working with images, see the [segment_anything_2_image repository](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/segment_anything_2_image) -![sam2](./Sam2Video.gif) +![sam2](/tutorials/Sam2Video.gif) ## Running from source @@ -77,4 +81,4 @@ If you want to contribute to this repository to help with some of these limitati ## Customization -The ML backend can be customized by adding your own models and logic inside the `./segment_anything_2_video` directory. \ No newline at end of file +The ML backend can be customized by adding your own models and logic inside the `./segment_anything_2_video` directory. diff --git a/docs/source/tutorials/segment_anything_model.md b/docs/source/tutorials/segment_anything_model.md index b7a7a1a6eb55..a6460cfff782 100644 --- a/docs/source/tutorials/segment_anything_model.md +++ b/docs/source/tutorials/segment_anything_model.md @@ -17,6 +17,10 @@ categories: image: "/tutorials/segment-anything.png" --- + + # Interactive annotation in Label Studio with Segment Anything Model https://github.com/shondle/label-studio-ml-backend/assets/106922533/42a8a535-167c-404a-96bd-c2e2382df99a diff --git a/docs/source/tutorials/sklearn_text_classifier.md b/docs/source/tutorials/sklearn_text_classifier.md index ee4102ed65f3..454d1ca59983 100644 --- a/docs/source/tutorials/sklearn_text_classifier.md +++ b/docs/source/tutorials/sklearn_text_classifier.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/scikit-learn.png" --- + + # Sklearn Text Classifier model for Label Studio The Sklearn Text Classifier model is a custom machine learning backend for Label Studio. It uses a [Logistic Regression model from the Scikit-learn](https://scikit-learn.org/) library to classify text data. This model is particularly useful for text classification tasks in Label Studio, providing an efficient way to generate pre-annotations based on the model's predictions. diff --git a/docs/source/tutorials/spacy.md b/docs/source/tutorials/spacy.md index 3a2567f6069e..e220c32fae40 100644 --- a/docs/source/tutorials/spacy.md +++ b/docs/source/tutorials/spacy.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/spacy.png" --- + + This ML backend provides a simple way to use [spaCy](https://spacy.io/) models for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. Current implementation includes the following models: diff --git a/docs/source/tutorials/tesseract.md b/docs/source/tutorials/tesseract.md index 00d9b843b7d0..a4221a74ad2b 100644 --- a/docs/source/tutorials/tesseract.md +++ b/docs/source/tutorials/tesseract.md @@ -14,6 +14,10 @@ categories: image: "/tutorials/tesseract.png" --- + + # Interactive bounding boxes OCR using Tesseract Use an OCR engine for interactive ML-assisted labeling, facilitating faster @@ -43,7 +47,7 @@ Launch Label Studio. You can follow the guide from the [official documentation]( docker run -it \ -p 8080:8080 \ -v `pwd`/mydata:/label-studio/data \ - heartex/label-studio:latest + heartexlabs/label-studio:latest ``` Optionally, you may enable local file serving in Label Studio @@ -54,7 +58,7 @@ Launch Label Studio. You can follow the guide from the [official documentation]( -v `pwd`/mydata:/label-studio/data \ --env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true \ --env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/data/images \ - heartex/label-studio:latest + heartexlabs/label-studio:latest ``` If you're using local file serving, be sure to [get a copy of the API token](https://labelstud.io/guide/user_account#Access-token) from Label Studio to connect the model. @@ -171,4 +175,4 @@ Example below: Reference links: - https://labelstud.io/blog/Improve-OCR-quality-with-Tesseract-and-Label-Studio.html -- https://labelstud.io/blog/release-130.html \ No newline at end of file +- https://labelstud.io/blog/release-130.html diff --git a/docs/source/tutorials/watsonx_llm.md b/docs/source/tutorials/watsonx_llm.md index 2f54a1bf321c..abfd477bd703 100644 --- a/docs/source/tutorials/watsonx_llm.md +++ b/docs/source/tutorials/watsonx_llm.md @@ -7,12 +7,16 @@ hide_menu: true hide_frontmatter_title: true meta_title: Integrate WatsonX with Label Studio categories: - - Computer Vision + - Generative AI - Large Language Model - WatsonX image: "/tutorials/watsonx.png" --- + + # Integrate WatsonX to Label Studio WatsonX offers a suite of machine learning tools, including access to many LLMs, prompt @@ -163,4 +167,4 @@ To get the host and port information below, you can follow the steps under [Pre- - `WATSONX_ENG_PORT` - the port information for your WatsonX.data Engine - `WATSONX_CATALOG` - the name of the catalog for the table you'll insert your data into. Must be created in the WatsonX.data platform. - `WATSONX_SCHEMA` - the name of the schema for the table you'll insert your data into. Must be created in the WatsonX.data platform. -- `WATSONX_TABLE` - the name of the table you'll insert your data into. Does not need to be already created. \ No newline at end of file +- `WATSONX_TABLE` - the name of the table you'll insert your data into. Does not need to be already created.