Train a Faster-RCNN object detection model on CVAT data with containerized MMDetection. Training can be visualized with Tensorboard. For more details, see Learn How to Train Object Detection Models With MMDetection
You can create an account for the free online version app.cvat.ai or run your own server.
After labeling your data, export a train and a test dataset in the COCO 1.0 format. This will give you images and annotations folders, which should be placed in the data folder in train and test.
Build an image with sudo docker build -t mmdet:latest .
pip install tensorboard
Run the image with the following command (modify path to mmdet folder):
docker run --gpus all \
-v "/path/to/mmdet/my_configs:/mmdetection/my_configs:ro" \
-v "/path/to/mmdet/data:/mmdetection/data:ro" \
-v "/path/to/mmdet/workdir:/mmdetection/workdir" \
mmdet:latest
To visualize the training process, cd to the latest folder in workdir and run tensorboard --logdir vis_data/.
In your browser, open http://localhost:6006/.
- MMDetection for model training
- CVAT for annotating and exporting training/testing data
- TensorBoard for training visualization
- blender-gen to create synthetic object detection data