YannAhlgrim
73 lines
2.7 KiB
Markdown
73 lines
2.7 KiB
Markdown
# WILDS-IJEPA
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Fork of the official I-JEPA repo, adapted for WILDS-iWildCam.
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Reference: official I-JEPA README https://github.com/facebookresearch/ijepa/blob/main/README.md
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- SSL pretraining on WILDS-iWildCam unlabeled dataset: https://arxiv.org/abs/2112.05090 (Extending the WILDS Benchmark for Unsupervised Adaptation)
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- Supervised training on WILDS-iWildCam labeled dataset: https://arxiv.org/abs/2012.07421 (WILDS: A Benchmark of in-the-Wild Distribution Shifts)
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- Supervised learning supports full fine-tuning or freezing the encoder
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<!-- Optional: add a pipeline figure (SSL -> SL) here -->
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## Models
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- ViT-H, 14x14 patches, 224x224 resolution (trained)
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- ViT-H, 16x16 patches, 448x448 resolution (trained)
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- Plan: add a graph comparing models with the WILDS leaderboard https://wilds.stanford.edu/leaderboard/#with-unlabeled-data-1
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<!-- Optional: add a WILDS leaderboard comparison graph here -->
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## Repo layout
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- `src/`: core model, masks, and training utilities
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- `src/train.py`: SSL training loop
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- `src/train_supervised.py`: supervised training loop
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- `configs/`: training configs
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- `configs/wilds_vith14_ep300.yaml`: SSL config used here
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- `configs/supervised_vith14_224.yaml`: supervised config used here (see `configs/` for all supervised linear-probe configs)
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- `main_distributed.py`: entrypoint for distributed SSL training
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- `main_distributed_supervised.py`: entrypoint for distributed supervised training
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- `requirements.txt`: dependencies
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<!-- Optional: add a sample iWildCam image grid here -->
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## Requirements
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- Python 3.8+ (compatible and newer)
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- PyTorch (CUDA 12.1 wheel index): https://download.pytorch.org/whl/cu121
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- Key deps: torchvision, submitit, wilds, PyYAML, numpy
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- Full list: `requirements.txt`
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## SLURM commands
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SSL pretraining:
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```
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python3 main_distributed.py --fname configs/wilds_vith14_ep300.yaml --folder $submitit_folder --partition $slurm_partition --nodes $nodes --tasks-per-node $tasks_per_node --time $time
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```
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Supervised fine-tuning:
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```
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python3 main_distributed_supervised.py --fname configs/supervised_vith14_224.yaml --folder $submitit_folder --partition $slurm_partition --nodes $nodes --tasks-per-node $tasks_per_node --time $time
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```
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Evaluation on iWildCam test split:
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```
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python3 main_eval_wilds.py --fname configs/eval_wilds_vith14.yaml --folder $submitit_folder --partition $slurm_partition --nodes $nodes --tasks-per-node $tasks_per_node --time $time
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```
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Evaluation metrics are written to `experiment_logs/eval-wilds-vith14/iwildcam_test_metrics.json` by default.
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Variable hints: set `$submitit_folder`, `$slurm_partition`, `$nodes`, `$tasks_per_node`, and `$time` to match your SLURM cluster.
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## License
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See the `LICENSE` file for details about the license under which this code is made available.
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## Citation
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To be defined.
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