| --- |
| license: cc-by-nc-sa-4.0 |
| library_name: pytorch |
| base_model: openai/clip-vit-large-patch14-336 |
| tags: |
| - computer-vision |
| - anomaly-detection |
| - anomaly-segmentation |
| - zero-shot-learning |
| - industrial-anomaly-detection |
| - clip |
| - fe-clip |
| - mvtec-ad |
| - visa |
| - pytorch |
| --- |
| |
| # FE-CLIP Reproduction Checkpoints: MVTec AD & VisA |
|
|
| This repository provides PyTorch checkpoints from an independent reconstruction of: |
|
|
| **FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation** |
| Tao Gong, Qi Chu, Bin Liu, Wei Zhou, and Nenghai Yu |
| ICCV 2025, pages 21220–21230. |
|
|
| - [Official paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Gong_FE-CLIP_Frequency_Enhanced_CLIP_Model_for_Zero-Shot_Anomaly_Detection_and_ICCV_2025_paper.pdf) |
| - [Supplementary material](https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gong_FE-CLIP_Frequency_Enhanced_ICCV_2025_supplemental.pdf) |
|
|
| > [!IMPORTANT] |
| > These are independently reproduced research checkpoints. They are not official checkpoints released by the FE-CLIP authors, and this repository is not affiliated with the original authors. |
|
|
| ## Repository Contents |
|
|
| The trained checkpoints are distributed as a single archive: |
|
|
| ```text |
| FE-CLIP.zip |
| ``` |
|
|
| Archive information: |
|
|
| | Property | Value | |
| |---|---:| |
| | Compressed size | 1,824,019,956 bytes | |
| | Displayed size | Approximately 1.82 GB | |
| | Uncompressed size | Approximately 1.85 GiB | |
| | PyTorch checkpoints | 18 | |
| | Training-history files | 2 | |
| | SHA-256 | `7821723A70AD54720F78D46F46D0E812F26B0D394280C8EAA5D8A76A19751499` | |
|
|
| The ZIP contains the following structure: |
|
|
| ```text |
| FE-CLIP/ |
| ├── train_on_mvtec_seed_111/ |
| │ ├── feclip_train_on_mvtec_epoch_01.pth |
| │ ├── feclip_train_on_mvtec_epoch_02.pth |
| │ ├── feclip_train_on_mvtec_epoch_03.pth |
| │ ├── feclip_train_on_mvtec_epoch_04.pth |
| │ ├── feclip_train_on_mvtec_epoch_05.pth |
| │ ├── feclip_train_on_mvtec_epoch_06.pth |
| │ ├── feclip_train_on_mvtec_epoch_07.pth |
| │ ├── feclip_train_on_mvtec_epoch_08.pth |
| │ ├── feclip_train_on_mvtec_epoch_09.pth |
| │ └── history.json |
| └── train_on_visa_seed_111/ |
| ├── feclip_train_on_visa_epoch_01.pth |
| ├── feclip_train_on_visa_epoch_02.pth |
| ├── feclip_train_on_visa_epoch_03.pth |
| ├── feclip_train_on_visa_epoch_04.pth |
| ├── feclip_train_on_visa_epoch_05.pth |
| ├── feclip_train_on_visa_epoch_06.pth |
| ├── feclip_train_on_visa_epoch_07.pth |
| ├── feclip_train_on_visa_epoch_08.pth |
| ├── feclip_train_on_visa_epoch_09.pth |
| └── history.json |
| ``` |
|
|
| Each checkpoint is approximately 105 MiB. |
|
|
| ## Which Checkpoint Should I Use? |
|
|
| For normal evaluation, use the final epoch-9 checkpoint. |
|
|
| | Target dataset | Checkpoint to use | |
| |---|---| |
| | MVTec AD | `train_on_visa_seed_111/feclip_train_on_visa_epoch_09.pth` | |
| | VisA | `train_on_mvtec_seed_111/feclip_train_on_mvtec_epoch_09.pth` | |
| | Other zero-shot datasets | Start with the MVTec-trained epoch-9 checkpoint | |
|
|
| This direction is intentional. FE-CLIP follows a cross-dataset zero-shot anomaly-detection protocol: |
|
|
| - The MVTec-trained checkpoint is evaluated on VisA and other target datasets. |
| - The VisA-trained checkpoint is evaluated on MVTec AD. |
| - No training images from the target dataset should be used during zero-shot evaluation. |
|
|
| Epochs 1–8 are included for learning-curve analysis, ablation studies, checkpoint selection, and resuming experiments. |
|
|
| ## Download and Extract |
|
|
| ### Hugging Face CLI |
|
|
| Replace the repository name below with the actual repository ID: |
|
|
| ```bash |
| hf download Parsagh1383/YOUR_REPOSITORY_NAME FE-CLIP.zip --local-dir . |
| ``` |
|
|
| Extract it with: |
|
|
| ```bash |
| unzip FE-CLIP.zip |
| ``` |
|
|
| ### Python |
|
|
| ```python |
| from pathlib import Path |
| from zipfile import ZipFile |
| |
