Add PoundNet model card

README.md

@@ -1,3 +1,132 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-classification
+library_name: pytorch
+base_model: openai/clip-vit-large-patch14
+tags:
+- ai-generated-image-detection
+- deepfake-detection
+- synthetic-image-detection
+- image-forensics
+- clip
+- vision-language-model
+- prompt-learning
+- pytorch-lightning
+- arxiv:2408.08412
+---
+
+# PoundNet
+
+PoundNet checkpoint weights for the paper **"Penny-Wise and Pound-Foolish in AI-Generated Image Detection"**.
+
+PoundNet is a CLIP-based AI-generated image detector built around asymmetric prompt learning for binary real/fake classification and category-aware supervision. The method is designed to reduce the "penny-wise and pound-foolish" behavior of deepfake detectors: strong performance on a narrow training distribution but poor generalization and degraded upstream semantic knowledge.
+
+These weights are released for use with the official PoundNet codebase:
+
+- Code: https://github.com/iamwangyabin/PoundNet
+- arXiv: https://arxiv.org/abs/2408.08412
+- Model weights: https://huggingface.co/nebula/PoundNet
+
+## Model Details
+
+- **Architecture**: PoundNet
+- **Backbone**: CLIP ViT-L/14
+- **Task**: binary AI-generated image detection / deepfake detection
+- **Output**: real/fake prediction scores through the official evaluation code
+- **Training data**: ProGAN split from the ForenSynths-style training setup used by the official PoundNet implementation
+- **Checkpoint format**: PyTorch Lightning `.ckpt`
+
+The checkpoints in this repository are not standalone `transformers` checkpoints. They should be loaded with the official PoundNet repository and configuration files.
+
+## Released Checkpoints
+
+| Checkpoint | File |
+| --- | --- |
+| `poundnet_ViTL_Progan_20240506_23_30_25` | `poundnet_ViTL_Progan_20240506_23_30_25/last.ckpt` |
+| `poundnet_ViTL_Progan_20240804_21_16_47` | `poundnet_ViTL_Progan_20240804_21_16_47/last.ckpt` |
+| `poundnet_ViTL_Progan_20240805_10_31_08` | `poundnet_ViTL_Progan_20240805_10_31_08/last.ckpt` |
+| `poundnet_ViTL_Progan_20240805_12_09_21` | `poundnet_ViTL_Progan_20240805_12_09_21/last.ckpt` |
+
+## Installation
+
+Clone the official repository and install dependencies:
+
+```bash
+git clone https://github.com/iamwangyabin/PoundNet.git
+cd PoundNet
+pip install -r requirements.txt
+```
+
+Install PyTorch separately according to your CUDA environment before installing the remaining dependencies.
+
+## Download Weights
+
+```bash
+mkdir -p weights
+
+wget -O ./weights/poundnet_ViTL_Progan_20240506_23_30_25.ckpt \
+  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240506_23_30_25/last.ckpt
+
+wget -O ./weights/poundnet_ViTL_Progan_20240804_21_16_47.ckpt \
+  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240804_21_16_47/last.ckpt
+
+wget -O ./weights/poundnet_ViTL_Progan_20240805_10_31_08.ckpt \
+  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240805_10_31_08/last.ckpt
+
+wget -O ./weights/poundnet_ViTL_Progan_20240805_12_09_21.ckpt \
+  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240805_12_09_21/last.ckpt
+```
+
+## Evaluation
+
+PoundNet expects benchmark datasets saved in Hugging Face Arrow format and loaded through `datasets.load_from_disk(...)`. See the official repository for the expected dataset layout and download helper.
+
+Example evaluation command:
+
+```bash
+python test.py --cfg cfgs/poundnet.yaml \
+  datasets.base_path=/path/to/DF-arrow
+```
+
+The default evaluation config uses the ViT-L/14 PoundNet checkpoint and evaluates on multiple AI-generated image detection benchmarks through the official codebase.
+
+## Intended Uses
+
+PoundNet is intended for academic research on:
+
+- AI-generated image detection
+- deepfake detection
+- synthetic image forensics
+- cross-generator and cross-dataset generalization
+- prompt-learning adaptation of vision-language models
+
+## Limitations
+
+These checkpoints are research artifacts and should not be treated as a complete production moderation or forensic system. Performance can vary under distribution shifts such as unseen generators, image editing pipelines, social media compression, resizing, screenshots, adversarial post-processing, or domain-specific content.
+
+The released checkpoints require the official PoundNet code and configuration files. They are not directly loadable through `AutoModel.from_pretrained`.
+
+## Ethical Considerations
+
+PoundNet is released to support research on synthetic media detection and trustworthy image forensics. Users should validate performance carefully before applying it to real-world moderation, legal, journalistic, or security workflows. Detection results should not be used as the sole evidence for high-stakes decisions.
+
+## Citation
+
+If you use PoundNet, please cite:
+
+```bibtex
+@article{wang2026pennywise,
+  title   = {Penny-Wise and Pound-Foolish in AI-Generated Image Detection},
+  author  = {Wang, Yabin and Huang, Zhiwu and Su, Zhou and Prugel-Bennett, Adam and Hong, Xiaopeng},
+  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  pages   = {1--14},
+  year    = {2026},
+  doi     = {10.1109/TPAMI.2026.3664388}
+}
+```
+
+## Links
+
+- Code: https://github.com/iamwangyabin/PoundNet
+- arXiv: https://arxiv.org/abs/2408.08412
+- Weights: https://huggingface.co/nebula/PoundNet