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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for timesformer_GP_scroll1 |
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The grandprize winning model of the Vesuvius Challenge of 2023. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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The grandprize winning model of the Vesuvius Challenge of 2023. |
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The model features a small TimeSformer architecture trained on image segmentation task to detect ink in 3d images. |
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This model takes as input the 3d image and outputs a 2d map of ink detections, roughly 1/16 the size of the input. |
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- **Developed by:** Youssef Nader as part of the Grandprize Winning Team |
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- **Model type:** TimeSformer |
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- **License:** MIT |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/younader/Vesuvius-Grandprize-Winner **[archived]** |
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Active development resumed here: https://github.com/ScrollPrize/villa |
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### How to Get Started with the Model |
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Make sure to have the dependencies installed, namely transformers and <a href="https://github.com/lucidrains/TimeSformer-pytorch">Timesformer package</a> |
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```bash |
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pip install -U transformers timesformer-pytorch |
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``` |
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Next you can run the model as follows: |
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```python |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("YoussefMoNader/timesformer_GP_scroll1", trust_remote_code=True) |
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``` |
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the model expects a (B,1,26,64,64) tensor |
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<!-- ## Training Details |
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<!-- ### Training Data |
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#### Hardware |
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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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<!-- ## Technical Specifications [optional] |
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### Model Architecture and Objective |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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<!-- ## Citation [optional] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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## Model Card Authors [optional] |
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