SSL pretraining on WILDS-iWildCam unlabeled dataset: https://arxiv.org/abs/2112.05090 (Extending the WILDS Benchmark for Unsupervised Adaptation)
Supervised training on WILDS-iWildCam labeled dataset: https://arxiv.org/abs/2012.07421 (WILDS: A Benchmark of in-the-Wild Distribution Shifts)
Supervised learning supports full fine-tuning or freezing the encoder

Models

ViT-H, 14x14 patches, 224x224 resolution (trained)
ViT-H, 16x16 patches, 448x448 resolution (trained)
Plan: add a graph comparing models with the WILDS leaderboard https://wilds.stanford.edu/leaderboard/#with-unlabeled-data-1

Repo layout

src/: core model, masks, and training utilities
src/train.py: SSL training loop
src/train_supervised.py: supervised training loop
configs/: training configs
configs/wilds_vith14_ep300.yaml: SSL config used here
configs/supervised_vith14_224.yaml: supervised config used here (see configs/ for all supervised linear-probe configs)
main_distributed.py: entrypoint for distributed SSL training
main_distributed_supervised.py: entrypoint for distributed supervised training
configs/grids/seeds/: per-model seed grids for multi-seed paper runs
tools/run_seed_sweep.sh: launch each model across all seeds (one by one)
tools/aggregate_seeds.py: aggregate seed runs into mean +/- std (ID + OOD)
requirements.txt: dependencies

Requirements

Python 3.8+ (compatible and newer)
PyTorch (CUDA 12.1 wheel index): https://download.pytorch.org/whl/cu121
Key deps: torchvision, submitit, wilds, PyYAML, numpy
Full list: requirements.txt

SLURM commands

SSL pretraining:

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

Supervised fine-tuning:

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

Evaluation on iWildCam test split:

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

Evaluation metrics are written to experiment_logs/eval-wilds-vith14/iwildcam_test_metrics.json by default.

Variable hints: set $submitit_folder, $slurm_partition, $nodes, $tasks_per_node, and $time to match your SLURM cluster.

Multi-seed runs (paper results)

To report mean +/- std over seeds, each supervised model is trained across 5 seeds (0-4). Seeding is config-driven via meta.seed (applied in src/train_supervised.py), and the run folder name includes -seed{N} so seeds do not collide.

Each run automatically:

evaluates on both WILDS splits: id_test (ID) and test (OOD), so the generalization gap can be measured;
records the WILDS metrics, the wall-clock training time, the number of epochs run (accounting for early stopping), and the effective memory usage into the per-split metrics JSON and into params.yaml in the eval folder.

Effective memory is captured as a high-water mark during training:

peak_host_ram_gb: peak process RSS (resource.getrusage), to compare against the SLURM mem_per_gpu request (e.g. 180G) and right-size future jobs. With tasks_per_node: 1 this reflects the whole training worker.
peak_gpu_alloc_gb / peak_gpu_reserved_gb: peak GPU VRAM (torch.cuda.max_memory_allocated / max_memory_reserved).

The four leaderboard columns are: Test ID Macro F1, Test ID Avg Acc, Test OOD Macro F1, Test OOD Avg Acc (headline metric: F1-macro_all).

Launch all models, one at a time, each across all seeds (SLURM/submitit):

bash tools/run_seed_sweep.sh --partition $slurm_partition --time $time

Run a subset of models:

bash tools/run_seed_sweep.sh --partition $slurm_partition --models "vith14_224 vith16_448"

Per-model seed grids live in configs/grids/seeds/ (each sets meta.seed: [0, 1, 2, 3, 4] over the corresponding configs/supervised_*.yaml base config). They are launched via tools/run_grid.py.

Aggregate mean +/- std across seeds after the jobs finish:

python3 tools/aggregate_seeds.py --root experiment_logs/eval-wilds

Outputs:

experiment_logs/seed-runs/<model>/summary.json (per-seed rows + mean/std for all metrics, training time, epochs, peak memory, and ID-OOD generalization gap)
experiment_logs/seed-runs/summary_all.csv (one row per model, paper-ready; includes peak_host_ram_gb_mean/std and peak_gpu_alloc_gb_mean/std)

License

See the LICENSE file for details about the license under which this code is made available.

Citation

To be defined.