Setup

Setup guide for a label studio instance with a yolo(v7) backend

1. Create a virtual environment for label studio

   python -m venv label-studio

   .\label-studio\Scripts\activate or on linux: source ./env/bin/activate

2. Clone this repo & install the required packages

   git clone https://github.com/4rn3/yolov7_labelstudio.git
cd code
pip install -r requirements.txt
   

3. Setting up label studio

   git clone https://github.com/heartexlabs/label-studio.git

   pip install label-studio

   Label studio is properly set up if using the label-studio command opens label studio in a new browser tab.

4. Git clone the yolov7 repo

   git clone https://github.com/WongKinYiu/yolov7.git

5. Setting up label studio ml backend

   git clone https://github.com/heartexlabs/label-studio-ml-backend

   cd label-studio-ml-backend

   pip install -U -e .

   cd ..

   pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
   (Note: currently pytorch on windows only supports python versions 3.7-3.9)

   label-studio-ml init backend --from .\model_backend.py --force

   If everything went well you should be able to run the label-studio-ml start .\backend command, if there are errors look at point 6 first.

6. Installing missing modules

   if there are errors about missing modules when running the label-studio-ml backend install them via pip
   The pip install commands can be found here https://pypi.org/project/

7. Setting up the .env file
   

   rename the .example_env file to .env
   The first field LABEL_STUDIO_HOST is the addres of the label studio instance.
   The second field LABEL_STUDIO_API_KEY is your label-studio api key this can be found in label studio under account settings (click the cricle with your initials) > Account & Settings > Access Token

8. Connecting the backend

   Go to your project, settings > general > Task sampling > select Sequential sampling > click save
   Go to the labeling interface to setup the labels, add Platelets in the "add labels names" box and click "Add". Repeat this for RBC and WBC
   Start the backend with label-studio-ml start .\backend (it should start without errors)
   Head over to settings > Machine Learning and click "Add Model"
   Give the backend a title and add its address i.e http://localhost:9090
   (optional) set a description
   select "Use for interactive preannotation"
   In the "ML-Assisted Labeling" section enable all 3 buttons
   Click save
   Go back to the main project view > click on the order type > Prediction score (make sure the arrow is pointing down)
   

   Now the images should be preannotated, the preannotations are then ordered based on score to more easily improve the bad annotations. The model is updated with each submition or update of an annotation.

9. Bug in yolov7 loss.py

   At the moment (15/12/2022) there seems to be a bug in the loss.py file of the yolov7 implementation. It results in the error: "RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)" on line 759. The fixed that worked for me was to change line 742 to matching_matrix = torch.zeros_like(cost, device="cpu")