docker pull heartexlabs/label-studio:latest
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data \
--env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true \
--env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/files \
-v $(pwd)/myfiles:/label-studio/files \
heartexlabs/label-studio:latest label-studio
#es.
docker run -d -p 8080:8080 --restart=always --name label-studio -v $(pwd)/mydata:/label-studio/data --env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true --env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/files -v /ai-data/project/datasets:/label-studio/files heartexlabs/label-studio:latest label-studio- Open the Label Studio UI at http://localhost:8080.
- Sign up with an email address and password that you create.
- Name the project, and if you want, type a description and select a color.
- Click Data Import and upload the data files that you want to use. If you want to use data from a local directory
- Click Labeling Setup and choose a template and customize the label names for your use case or using a custom configuration that you create from scratch using tags.
- Click Save to save your project.
This is a custom configuration for object detections task.
<View>
<Image name="image" value="$image"/>
<Header value="RectangleLabels"/>
<RectangleLabels name="label" toName="image">
<Label value="person" predicted_values="person"/>
</RectangleLabels>
</View>- Open Label Studio in your web browser.
- For a specific project, open Settings > Cloud Storage.
- Click Add Source Storage.
- In the dialog box that appears, select Local Files as the storage type.
- In the Storage Title field, type a name for the storage to appear in the Label Studio UI.
- Specify an Absolute local path to the directory with your files. The local path must be an absolute path and include the LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT value.
- Click Add Storage.
For example, if LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/files, then your local path must be /label-studio/files/MTT/frames2301/images
Clone repostory with submodules
git clone https://github.com/mottajacopo/LS-yolov5.git
git submodule update --init --recursiveInstall requirements
pip install -r yolov5/requirements.txt # yolo requirements
pip install -U -e label-studio-ml-backend # install label studio backend
pip install redis rq # additional libraries for the backendcd LS-yolov5
export LABEL_STUDIO_ML_BACKEND_V2=true
wget -O yolov5.pt https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt
label-studio-ml init minecraft_copilot --script ./main.py --force
label-studio-ml start minecraft_copilotThe ML backend server becomes available at http://localhost:9090
cd LS-yolov5
docker build -t label-studio-yolov5-backend .
docker run -it --name label-studio-yolov5-ml-backend \
-p 9090:9090 --gpus all --shm-size=8192M \
-v path-to-local-storage:/data/local-files \
-v path-to-label-studio-upload-folder:/data/upload \
label-studio-yolov5-backend
#es.
docker run -d --restart=always --name label-studio-yolov5-ml-backend -p 9090:9090 --gpus all --shm-size=8192M -v /ai-data/project/datasets/:/data/local-files -v /ai-data/project/LabelStudio/mydata/media/upload/:/data/upload label-studio-yolov5-backendAdd an ML backend using the Label Studio UI
- In the Label Studio UI, open the project that you want to use with your ML backend.
- Click Settings > Machine Learning.
- Click Add Model.
- Type a Title for the model and provide the URL for the ML backend. For example, http://localhost:9090.
- Click Validate and Save.
NOTE: At the moment only auto labeling using pre-trained models is supported. Training not yet implemented.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent