| --- |
| license: other |
| license_name: mixed-see-model-card |
| license_link: LICENSE |
| tags: |
| - concept-erasure |
| - machine-unlearning |
| - evaluation |
| - image-classification |
| - diffusion-models |
| - EMMA |
| library_name: pytorch |
| --- |
| |
| # EMMA — Classifier / Evaluation Checkpoints |
|
|
| Evaluation-only classifier checkpoints used by the **EMMA** concept-erasure benchmark |
| ([github.com/lobsterlulu/EMMA](https://github.com/lobsterlulu/EMMA/tree/main/classifier)). |
|
|
|
|
| --- |
|
|
| ## 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](https://github.com/OPTML-Group/Diffusion-MU-Attack) (MIT) · |
| [paper](https://arxiv.org/abs/2310.11868) |
|
|
| ### `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](https://github.com/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](https://github.com/Alibaba-MIIL/ML_Decoder) (MIT) · |
| [paper](https://arxiv.org/abs/2111.12933) |
|
|
| ### `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: |
|
|
| ```bash |
| 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](https://github.com/ultralytics/ultralytics) (AGPL-3.0) |
|
|
| --- |
|
|
| ## Download |
|
|
| ### Everything (~1.9 GB) |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| 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 |
|
|
| ```python |
| 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`](SHA256SUMS): |
|
|
| ```bash |
| cd emma-weights && sha256sum -c SHA256SUMS |
| ``` |
|
|
| --- |
|
|
| ## 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. |
| |
| ```bibtex |
| @misc{emma, |
| title = {EMMA}, |
| author = {Lu, Wei and others}, |
| year = {2026}, |
| url = {https://github.com/lobsterlulu/EMMA} |
| } |
| ``` |
| |
