docker-compose.yml

version: "3.8"

services:
  mmdetection-3:
    container_name: mmdetection-3
    image: heartexlabs/label-studio-ml-backend:mmdetection3-master
    build:
      context: .
      args:
        TEST_ENV: ${TEST_ENV}
    environment:
      # specify these parameters if you want to use basic auth for the model server
      - BASIC_AUTH_USER=
      - BASIC_AUTH_PASS=
      # set the log level for the model server
      - LOG_LEVEL=DEBUG
      # specify the number of workers and threads for the model server
      - WORKERS=1
      - THREADS=8
      # specify the model directory (likely you don't need to change this)
      - MODEL_DIR=/data/models
      # mmdetection model parameters
      - CHECKPOINT_FILE=yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth
      - CONFIG_FILE=yolov3_mobilenetv2_8xb24-320-300e_coco.py
      - DEVICE=cpu
      - SCORE_THRESHOLD=0.5

      # Specify the Label Studio URL and API key to access
      # uploaded, local storage and cloud storage files.
      # Do not use 'localhost' as it does not work within Docker containers.
      # Use prefix 'http://' or 'https://' for the URL always.
      # Determine the actual IP using 'ifconfig' (Linux/Mac) or 'ipconfig' (Windows).
      - LABEL_STUDIO_URL=
      - LABEL_STUDIO_API_KEY=

      # for s3 storage you can specify AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY,
      # it will work faster than using intermediate LS API for your file URL resolving from s3
      - AWS_ACCESS_KEY_ID=
      - AWS_SECRET_ACCESS_KEY=
    ports:
      - "9090:9090"
    volumes:
      - "./data/server:/data"