Accelerated DL R&D

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

Catalyst at AI Landscape.

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Catalyst.Detection

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

0. Install requirements pip install -r requirements.txt

1. Copy all images to one directory or two different directories for train and validation.

2. Create markup_train.json as json file in MSCOCO format using COCODetectionFactory from 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.

3. Specify perameters and in config/centernet_detection_config.yml.

4. Run catalyst catalyst-dl run --config=./configs/centernet_detection_config.yml

5. When you change dataset, you must delete cache files markup_*.json.cache because this files contain preprocessed bounding boxes info.