PyTorch framework for Deep Learning research and development.
It was developed with a focus on reproducibility,
fast experimentation and code/ideas reusing.
Being able to research/develop something new,
rather than write another regular train loop.
Break the cycle - use the Catalyst!
Project manifest. Part of PyTorch Ecosystem. Part of Catalyst Ecosystem:
- Alchemy - Experiments logging & visualization
- Catalyst - Accelerated Deep Learning Research and Development
- Reaction - Convenient Deep Learning models serving
Note: this repo uses advanced Catalyst Config API and could be a bit out-of-day right now. Use Catalyst's minimal examples section for a starting point and up-to-day use cases, please.
Based on Objects as points article by Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
Training in your dataset
pip install -r requirements.txt
Copy all images to one directory or two different directories for train and validation.
markup_train.jsonas json file in MSCOCO format using
data_preparation.py. This class may be copied to your dataset generator. See documentation in code comments. If your dataset are already in this format, go to next step.
Specify perameters and in
catalyst-dl run --config=./configs/centernet_detection_config.yml
When you change dataset, you must delete cache files
markup_*.json.cachebecause this files contain preprocessed bounding boxes info.