---
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