docs: Update label-studio-ml-backend docs

docs/source/guide/ml_tutorials.html

@@ -335,22 +335,6 @@
   title: YOLO ML Backend for Label Studio
   type: guide
   url: /tutorials/yolo.html
-- categories:
-  - Computer Vision
-  - Video Classification
-  - Temporal Labeling
-  - LSTM
-  hide_frontmatter_title: true
-  hide_menu: true
-  image: /tutorials/yolo-video-classification.png
-  meta_description: Tutorial on how to use an example ML backend for Label Studio
-    with TimelineLabels
-  meta_title: TimelineLabels ML Backend for Label Studio
-  order: 51
-  tier: all
-  title: TimelineLabels ML Backend for Label Studio
-  type: guide
-  url: /tutorials/yolo_timeline_labels.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.

docs/source/tutorials/gliner.md

@@ -82,4 +82,17 @@ The following common parameters are available:
 - `WORKERS` - Specify the number of workers for the model server.
 - `THREADS` - Specify the number of threads for the model server.
 - `LABEL_STUDIO_URL` - Specify the URL of your Label Studio instance. Note that this might need to be `http://host.docker.internal:8080` if you are running Label Studio on another Docker container.
-- `LABEL_STUDIO_API_KEY`- Specify the API key for authenticating your Label Studio instance. You can find this by logging into Label Studio and and [going to the **Account & Settings** page](https://labelstud.io/guide/user_account#Access-token).
+- `LABEL_STUDIO_API_KEY`- Specify the API key for authenticating your Label Studio instance. You can find this by logging into Label Studio and and [going to the **Account & Settings** page](https://labelstud.io/guide/user_account#Access-token). 
+
+## A Note on Model Training 
+
+If you plan to use a webhook to train this model on "Start Training", note that you do 
+not need to configure a separate webhook. Instead, go to the three dots next to your model
+on the Model tab in your project settings and click "start training". 
+
+Additionally, note that this container has been set for a **VERY SMALL** demo set, with only 1
+non-eval sample (we expect the first 10 data samples to be for evaluation.)
+
+If you're working with a larger dataset, be sure to: 
+1. update num_steps and batch size to the number of training steps you want and the batch size that works for your dataset. 
+2. change the uploaded model after training (line 239 of `model.py`) to the highest checkpoint that you have.

docs/source/tutorials/mmdetection-3.md

@@ -23,7 +23,7 @@ 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](/tutorials/screenshot.png)
+![screenshot.png](screenshot.png)
 
 ## Before you begin
 
@@ -43,7 +43,7 @@ docker-compose up -d
 
 See the tutorial in the documentation for building your own image and advanced usage:
 
-https://github.com/HumanSignal/label-studio/blob/develop/docs/source/tutorials/object-detector.md
+https://github.com/HumanSignal/label-studio/blob/master/docs/source/tutorials/object-detector.md
 
 
 ## Labeling config
@@ -85,7 +85,7 @@ In this example, you can combine multiple labels into one Label Studio annotatio
 1. Clone the Label Studio ML Backend repository in your directory of choice:
 
 ```
-git clone https://github.com/HumanSignal/label-studio-ml-backend
+git clone https://github.com/heartexlabs/label-studio-ml-backend
 cd label-studio-ml-backend/label_studio_ml/examples/mmdetection-3
 ```
 
@@ -166,4 +166,4 @@ 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.
+* 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.

docs/source/tutorials/segment_anything_2_image.md

@@ -131,19 +131,32 @@ cd label_studio_ml/examples/segment_anything_2_image
 pip install -r requirements.txt
 ```
 
-2. Download [`segment-anything-2` repo](https://github.com/facebookresearch/segment-anything-2) into the root directory. Install SegmentAnything model and download checkpoints using [the official Meta documentation](https://github.com/facebookresearch/segment-anything-2?tab=readme-ov-file#installation)
+2. Download [`segment-anything-2` repo](https://github.com/facebookresearch/sam2) into the root directory. Install SegmentAnything model and download checkpoints using [the official Meta documentation](https://github.com/facebookresearch/sam2?tab=readme-ov-file#installation)
+You should now have the following folder structure: 
+
+
+    | root directory 
+        | label-studio-ml-backend 
+            | label-studio-ml
+                | examples 
+                    | segment_anything_2_image
+        | sam2
+            | sam2
+            | checkpoints
 
 
 3. Then you can start the ML backend on the default port `9090`:
 
 ```bash
-cd ../
-label-studio-ml start ./segment_anything_2_image
+cd ~/sam2
+label-studio-ml start ../label-studio-ml-backend/label_studio_ml/examples/segment_anything_2_image
 ```
 
+Due to breaking changes from Meta [HERE](https://github.com/facebookresearch/sam2/blob/c2ec8e14a185632b0a5d8b161928ceb50197eddc/sam2/build_sam.py#L20), it is CRUCIAL that you run this command from the sam2 directory at your root directory. 
+
 4. Connect running ML backend server to Label Studio: go to your project `Settings -> Machine Learning -> Add Model` and specify `http://localhost:9090` as a URL. Read more in the official [Label Studio documentation](https://labelstud.io/guide/ml#Connect-the-model-to-Label-Studio).
 
-## Running with Docker (coming soon)
+## Running with Docker
 
 1. Start Machine Learning backend on `http://localhost:9090` with prebuilt image:
 

docs/source/tutorials/segment_anything_2_video.md

@@ -21,7 +21,7 @@ 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](/tutorials/Sam2Video.gif)
+![sam2](./Sam2Video.gif)
 
 ## Before you begin
 
@@ -83,4 +83,4 @@ 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.
+The ML backend can be customized by adding your own models and logic inside the `./segment_anything_2_video` directory.

docs/source/tutorials/segment_anything_model.md

@@ -280,6 +280,12 @@ to get a better understanding of the workflow when annotating with SAM.
 
 Use the `Alt` hotkey to alter keypoint positive and negative labels.
 
+First, select either the auto-keypoints or auto-rectangle tool, then choose a label. You can now draw keypoints or rectangles on the canvas.
+Watch this video:
+
+https://github.com/user-attachments/assets/28acf6ae-a83f-4919-9722-3c82a4b6dab6
+
+
 ### Notes for AdvancedSAM
 
 * _**Please watch [this video](https://drive.google.com/file/d/1OMV1qLHc0yYRachPPb8et7dUBjxUsmR1/view?usp=sharing) first**_

docs/source/tutorials/tesseract.md

@@ -173,5 +173,5 @@ Example below:
 ![ls_demo_ocr](https://user-images.githubusercontent.com/17755198/165186574-05f0236f-a5f2-4179-ac90-ef11123927bc.gif)
 
 Reference links: 
-- https://labelstud.io/blog/improve-ocr-quality-for-receipt-processing-with-tesseract-and-label-studio
+- https://labelstud.io/blog/Improve-OCR-quality-with-Tesseract-and-Label-Studio.html
 - https://labelstud.io/blog/release-130.html