Upload README.md with huggingface_hub

README.md

@@ -1,12 +1,91 @@
 ---
-title: Ipad Vad Training
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
+title: IPAD VAD Training
+emoji: 🏭
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 5.49.1
+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
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏭 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)