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src | ||
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environment.yml | ||
LICENSE | ||
pyproject.toml | ||
README.md |
Neural sphere detection in images for lighting calibration
Installation
Clone the repository:
git clone https://github.com/Laurent2916/REVA-DETR.git
cd REVA-DETR/
Install and activate the environment:
micromamba install -f environment.yml
micromamba activate qcav
Usage
Everything is managed thanks to Lightning CLI !
Start a training:
python src/main.py fit
Start inference on images:
python src/main.py predict --ckpt_path <path_to_checkpoint>
Quick and dirty way to export to .onnx
:
>>> from src.module import DETR
>>> checkpoint = "<path_to_checkpoint>"
>>> model = DETR.load_from_checkpoint(checkpoint)
>>> model.net.save_pretrained("hugginface_checkpoint")
python -m transformers.onnx --model=hugginface_checkpoint onnx_export/
License
Distributed under the MIT license.
See LICENSE
for more information.
Contact
Laurent Fainsin [loʁɑ̃ fɛ̃zɛ̃]
<laurent@fainsin.bzh>