---
title: IPAD VAD Training
emoji: 🏭
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
tags:
  - video-anomaly-detection
  - industrial-ai
  - computer-vision
  - pytorch
  - swin-transformer
duplicated_from:
short_description: Train IPAD video anomaly detection models on ZeroGPU
hardware: zero-a100
---

# 🏭 IPAD: Industrial Process Anomaly Detection Training

Train state-of-the-art video anomaly detection models on industrial datasets using ZeroGPU.

## Features

- 🚀 **ZeroGPU Training**: Free access to NVIDIA H200 GPUs
- 📊 **12 Industrial Devices**: Synthetic training data for various equipment
- 🎯 **Baseline Model**: Video Swin Transformer + Periodic Memory Module
- 💾 **Automatic Checkpointing**: Save and upload trained models to HF Hub
- 📈 **Real-time Monitoring**: Track training progress with Gradio interface

## Quick Start

1. **Download Dataset**: Click "Download Dataset" to fetch the 8.3GB IPAD dataset from HF Hub
2. **Quick Test**: Verify GPU access with the quick test (no dataset required)
3. **Train Baseline**: Select a device and start training with default hyperparameters

## Dataset

The IPAD dataset contains:
- **16 Industrial Devices** (12 synthetic + 4 real)
- **597,979 Video Frames**
- **39 Anomaly Classes**
- **Frame-level Annotations**

[View Dataset →](https://huggingface.co/datasets/MSherbinii/ipad-industrial-anomaly)

## Model Architecture

```
Input (16 frames, 256×256)
    ↓
Video Swin Transformer Encoder
    ↓
Periodic Memory Module (2000-dim)
    ↓
I3D Decoder
    ↓
Reconstructed Output
```

## Baseline Results

| Device | AUC (%) | Device | AUC (%) |
|--------|---------|--------|---------|
| S01 | 69.5 | S07 | 60.6 |
| S02 | 63.9 | S08 | 85.6 |
| S03 | 70.6 | S09 | 71.2 |
| S04 | 58.3 | S10 | 62.2 |
| S05 | 86.2 | S11 | 60.9 |
| S06 | 61.2 | S12 | 67.1 |
| **Avg** | **68.6** | | |

## Citation

```bibtex
@article{liu2024ipad,
  title={IPAD: Industrial Process Anomaly Detection Dataset},
  author={Liu, Jinfan and Yan, Yichao and Li, Junjie and others},
  journal={arXiv preprint arXiv:2404.15033},
  year={2024}
}
```

## Links

- [Paper](https://arxiv.org/abs/2404.15033)
- [Original Code](https://github.com/LJF1113/IPAD)
- [Dataset](https://huggingface.co/datasets/MSherbinii/ipad-industrial-anomaly)