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---
library_name: transformers
tags: []
---
# Model Card for timesformer_GP_scroll1
The grandprize winning model of the Vesuvius Challenge of 2023.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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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