Instance segmentation framework for kaggle ship detection1 using Pytorch based toolboxes Detectron2 and Fastai.
Final presentation 2
- Requirements:
- Unix based OS (Linux/Mac OS).
- Nvidia GPU with cuda 10.1 installed, and at least 10GB of vram.
- Docker (version == 19.03) and docker-compose (version == 3.6)
- 40GB of free space: ~10GB for docker image, ~30GB for data
*All other dependencies are taken care of by docker-compose.
Data
kaggle competitions download -c airbus-ship-detection
Use the Kaggle API to download the dataset 3
Put it in folder named input
Model preprocessing / training and Kaggle submittion
docker-compose up
- Training: In the container, open the notebook
module_notebook
and run all. - Metrics: tensorboard for metrics, use another jupiter notebook and
!tensorboard --logdir=runs --host=0.0.0.0
and if run on a local machine use a browser to go to http://0.0.0.0:6006.
- Data Loader (module_preprocessing) 4
- Classifier (FastAI) 5
- Output probability of ship on image.
- Resnet34
- Instance Segmentation/Object Detection (Detectron2) 6
- Predictions on validation (module_submit)
- Kaggle submission (module_submit) 4
- Kaggle proptotype
- Initial presentation
- Data Loader module
- Training module
- Add data Augmentations
- Submit module
- Classification module
- Dockerize project
- Argumentize code
- Train
- Inference
- Submit