---
library_name: transformers
tags: []
---

# Model Card for timesformer_GP_scroll1

The grandprize winning model of the Vesuvius Challenge of 2023. 
 



## Model Details

### Model Description

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The grandprize winning model of the Vesuvius Challenge of 2023.    
The model features a small TimeSformer architecture trained on image segmentation task to detect ink in 3d images.    
This model takes as input the 3d image and outputs a 2d map of ink detections, roughly 1/16 the size of the input.    

- **Developed by:** Youssef Nader as part of the Grandprize Winning Team
- **Model type:** TimeSformer
- **License:** MIT

### Model Sources 

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- **Repository:** https://github.com/younader/Vesuvius-Grandprize-Winner **[archived]**    
Active development resumed here: https://github.com/ScrollPrize/villa



### How to Get Started with the Model
Make sure to have the dependencies installed, namely transformers and <a href="https://github.com/lucidrains/TimeSformer-pytorch">Timesformer package</a>
```bash
pip install -U transformers timesformer-pytorch
```

Next you can run the model as follows:

```python
from transformers import AutoModel
model = AutoModel.from_pretrained("YoussefMoNader/timesformer_GP_scroll1", trust_remote_code=True)
```
the model expects a (B,1,26,64,64) tensor
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#### Hardware

The model was trained on 4xH100 for 8 hours. This model was trained for 12 epochs on total, a single epoch takes around 45 mins using the old script train_timesformer_og.py 


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