I-JEPA deepfake detectors (per generator)

Fine-tune facebook/ijepa_vith14_1k (ViT-H/14) as a binary fake vs real detector, one model per generator.

Protocol

For each trainable generator G ∈
ADM, BigGAN, Midjourney, VQDM, glide, wukong, stable_diffusion_v_1_4, stable_diffusion_v_1_5, imagenetv3:

Split	Fakes	Reals
Train	`dataset/G/train/ai`	`dataset/train_nature` (shared)
Eval	every `dataset/*/val/ai`	matching `val/nature` or `val/real`

Never train on flux2 or drifting (test-only / OOD).
Labels: 0 = real, 1 = fake.
A 10% train holdout is used only for in-run monitoring (not a GenImage holdout).

Dual-GPU launch (not DDP)

run_all.py starts one process per GPU. Each process:

sets CUDA_VISIBLE_DEVICES=<gpu>
trains + evaluates one detector
shuts down DataLoader workers and frees CUDA
pulls the next unfinished generator until the queue is empty

Training uses a 5% balanced holdout from train/ai + train_nature (metric=acc, patience=4, checks every 1000 steps, max 1 epoch). Classes are balanced 50/50.

imagenetv3-only extras (other generators unchanged):

no patience (full epoch)
LR hypertune on a small split (2k/class, 800 steps, 1e-5…1e-4)
after training, pick last-epoch vs best-holdout on its own val split

cd /home/themis/workspace_firas/appli/i-jepa
conda activate test   # or any env with torch + transformers
pip install -r requirements.txt

# full sweep on GPU 0 and GPU 1
python run_all.py --gpus 0,1

# retrain ADM only (overwrite broken checkpoint)
python train.py --generator ADM --device cuda:0 --force

# smoke test
python run_all.py --gpus 0,1 --limit-per-class 200 --epochs 1

# subset
python run_all.py --gpus 0,1 --generators ADM,BigGAN

Single-generator (one GPU):

python train.py --generator ADM --device cuda:0

Metrics

Accuracy: argmax over 2-class logits
AUROC: ranking score = MSP (max softmax probability), against labels 0=real, 1=fake

To recompute AUROC with MSP for already-trained detectors (rewrites metrics.json / CSVs):

python recompute_auroc_msp.py --gpus 0,1
# or single GPU:
python recompute_auroc_msp.py --device cuda:0

Outputs

runs/<generator>/
  detector.pt
  train_log.json
  metrics.json
  metrics.csv
runs/summary_auroc.csv
runs/summary_acc.csv

Resume-safe:

If runs/<gen>/detector.pt exists → training is skipped
If runs/<gen>/metrics.json also exists → eval is skipped (job fully done)
If only the detector exists → eval-only resume
Use --force to retrain + re-eval from scratch

Memory / workers

Default --batch-size 8 + --amp bf16 for ViT-H. Raise batch if VRAM allows.
--num-workers 2 with persistent_workers=False; workers are shut down after every job.
Optional --freeze-backbone for a linear probe (much lighter).

Layout

File	Role
`config.py`	paths + hyperparameters
`data.py`	train/eval loaders
`model.py`	I-JEPA binary detector
`train.py`	train + cross-generator eval
`cleanup.py`	DataLoader / CUDA teardown
`run_all.py`	multi-GPU job queue
`upload_ijepa_hf.py`	upload this folder (code + `runs/`) to Hugging Face

Upload to Hugging Face

cd /home/themis/workspace_firas/appli/i-jepa
export HF_TOKEN=hf_...
python upload_ijepa_hf.py --repo-id fira7s/ijepa
# private / dataset / resumable workers:
python upload_ijepa_hf.py --repo-id fira7s/ijepa --private --num-workers 4