51 lines
1.2 KiB
Markdown
51 lines
1.2 KiB
Markdown
# Neural sphere detection in images for lighting calibration
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# Installation
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Clone the repository:
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```bash
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git clone https://github.com/Laurent2916/REVA-DETR.git
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cd REVA-DETR/
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```
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Install and activate the environment:
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```bash
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micromamba install -f environment.yml
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micromamba activate qcav
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```
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## Usage
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Everything is managed thanks to [Lightning CLI](https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.cli.LightningCLI.html#lightning.pytorch.cli.LightningCLI) !
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Start a training:
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```bash
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python src/main.py fit
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```
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Start inference on images:
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```bash
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python src/main.py predict --ckpt_path <path_to_checkpoint>
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```
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Quick and dirty way to export to `.onnx`:
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```python
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>>> from src.module import DETR
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>>> checkpoint = "<path_to_checkpoint>"
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>>> model = DETR.load_from_checkpoint(checkpoint)
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>>> model.net.save_pretrained("hugginface_checkpoint")
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```
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```bash
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python -m transformers.onnx --model=hugginface_checkpoint onnx_export/
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```
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## License
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Distributed under the [MIT](https://choosealicense.com/licenses/mit/) license. \
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See [`LICENSE`](https://github.com/Laurent2916/REVA-DETR/blob/master/LICENSE) for more information.
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## Contact
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Laurent Fainsin _[loʁɑ̃ fɛ̃zɛ̃]_ \
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\<[laurent@fainsin.bzh](mailto:laurent@fainsin.bzh)\>
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