weilulobster/EMMA-classifier-weights

EMMA — Classifier / Evaluation Checkpoints

Evaluation-only classifier checkpoints used by the EMMA concept-erasure benchmark (github.com/lobsterlulu/EMMA).

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Contents

The directory layout mirrors classifier/ in the GitHub repo, so files can be dropped straight into a clone.

Diffusion-MU-Attack/ — art-style classifier

ViT (ViTForImageClassification) fine-tuned over 129 artist styles (Unknown Artist, boris-kustodiev, ivan-shishkin, amedeo-modigliani, …). Consumed by src/utils/metrics/style_eval.py via a 🤗 image-classification pipeline.

File	Size	Needed for inference
classifier/checkpoint-2800/pytorch_model.bin	328 MB	yes
classifier/checkpoint-2800/config.json	8 KB	yes
classifier/checkpoint-2800/preprocessor_config.json	512 B	yes
classifier/checkpoint-2800/optimizer.pt	656 MB	no — resume only
classifier/checkpoint-2800/scheduler.pt	623 B	no — resume only
classifier/checkpoint-2800/scaler.pt	559 B	no — resume only
classifier/checkpoint-2800/rng_state.pth	15 KB	no — resume only
classifier/checkpoint-2800/training_args.bin	3.3 KB	no — resume only
classifier/checkpoint-2800/trainer_state.json	41 KB	no — resume only

If you only want to run evaluation, fetch pytorch_model.bin + the two JSON configs (~328 MB) and skip optimizer.pt entirely — it is a third of this repo's total size.

Upstream: OPTML-Group/Diffusion-MU-Attack (MIT) · paper

GCD/ — celebrity face recognition

Giphy Celebrity Detector: MTCNN face detection followed by a fine-tuned ResNet identity head over 2306 celebrity labels.

File	Size
resources/face_recognition/best_model_states.pkl	289 MB
resources/face_recognition/labels.csv	66 KB

The MTCNN weights (resources/face_detection/det{1,2,3}.npy) are small and already committed as plain files on GitHub — they are not duplicated here.

Upstream: Giphy/celeb-detection-oss (MPL-2.0 per upstream)

ML_Decoder/ — object / NSFW multi-label classification

File	Size	Classes	Notes
models_zoo/tresnet_l_COCO__448_90_0.pth	197 MB	80	MS-COCO, mAP 90.0 @ 448px. This is the checkpoint infer_nsfw.py defaults to.
tresnet_l.pth	192 MB	9605	TResNet-L Open Images backbone (ltresnet_v2, bottleneck head, epoch 37)
models_zoo/tresnet_l_stanford_card_96.41.pth	197 MB	196	Stanford Cars, 96.41%. Not used by the EMMA evaluation paths — included for completeness.

Note: infer.py ships with --model-path defaulting to ./models_local/TRresNet_L_448_86.6.pth, a path that does not exist in the repo. Pass --model-path models_zoo/tresnet_l_COCO__448_90_0.pth explicitly.

Upstream: Alibaba-MIIL/ML_Decoder (MIT) · paper

YOLO/ — brand-logo classification (copyright domain)

File	Size	Classes	What it is
model/logo_yolo11s_30cls.pt	11 MB	30	The EMMA-trained logo classifier. Fine-tuned from yolo11s-cls.pt, 100 epochs, 224×224.
model/yolo11n-cls.pt	5.6 MB	1000	stock Ultralytics ImageNet-1k
model/yolo11s-cls.pt	13 MB	1000	stock Ultralytics ImageNet-1k
model/yolo11x-cls.pt	57 MB	1000	stock Ultralytics ImageNet-1k
model/yolov8n.pt	6.3 MB	80	stock Ultralytics COCO detector

⚠️ Read this if you are reproducing the copyright / logo results. The four yolo11*-cls.pt / yolov8n.pt files are unmodified Ultralytics pretrained weights — they predict ImageNet or COCO classes, not brands. They are the starting point for training, not the evaluator. classifier/YOLO/run.py defaults to whichever .pt in model/ is largest (preferring yolo11x-cls), which will silently give you ImageNet predictions. Use the fine-tuned model explicitly:

python run.py --image_dir /path/to/images --model_path model/logo_yolo11s_30cls.pt

The 30 brand classes:

ASUS, Adidas SB, Apple, Asics, BMW, Barbie, Canon, Chevrolet, Colgate, Converse,
GUINNESS, Gap, Gillette, HTC, Heineken, Hot Wheels, Lacoste, Lamborghini, Marvel,
McDonald's, lexus, michelin, nestle, neutrogena, nivea, oakley, pantene, play-doh,
spalding, under armour

Framework: ultralytics/ultralytics (AGPL-3.0)

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Download

Everything (~1.9 GB)

pip install -U huggingface_hub
hf download weilulobster/EMMA-classifier-weights --local-dir ./emma-weights

Into an existing EMMA clone

The helper script in the GitHub repo places every file at its expected path:

git clone https://github.com/lobsterlulu/EMMA.git
cd EMMA/classifier
python download_weights.py              # inference-only set, ~1.2 GB
python download_weights.py --all        # include optimizer/scheduler state, ~1.9 GB
python download_weights.py --group yolo # one component only
python download_weights.py --verify     # re-check sha256 of what is on disk

Individual files

from huggingface_hub import hf_hub_download

p = hf_hub_download(
    "weilulobster/EMMA-classifier-weights",
    "ML_Decoder/models_zoo/tresnet_l_COCO__448_90_0.pth",
)

Every file's SHA-256 is recorded in SHA256SUMS:

cd emma-weights && sha256sum -c SHA256SUMS

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Licensing

This repository redistributes weights from several upstream projects; each retains its original license. There is no single license covering the whole repo.

Component	Origin	License
Diffusion-MU-Attack/	OPTML-Group/Diffusion-MU-Attack	MIT
ML_Decoder/	Alibaba-MIIL/ML_Decoder	MIT
GCD/	Giphy/celeb-detection-oss	MPL-2.0 (per upstream)
YOLO/ stock weights	Ultralytics	AGPL-3.0
YOLO/model/logo_yolo11s_30cls.pt	EMMA authors, fine-tuned from Ultralytics yolo11s-cls.pt	AGPL-3.0 (inherits)

Brand names in the logo classifier's label set are trademarks of their respective owners and appear only as class identifiers for research evaluation.

The celebrity recognition model carries the biases and consent limitations of its upstream training data. It is provided for reproducing erasure-benchmark numbers, not for identifying people in the wild.

Citation

If you use these checkpoints, please cite EMMA and the upstream classifier papers listed above.

@misc{emma,
  title  = {EMMA},
  author = {Lu, Wei and others},
  year   = {2026},
  url    = {https://github.com/lobsterlulu/EMMA}
}