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| library_name: transformers |
| tags: [] |
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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. |
| 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. |
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| - **Developed by:** Youssef Nader as part of the Grandprize Winning Team |
| - **Model type:** TimeSformer |
| - **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]** |
| Active development resumed here: https://github.com/ScrollPrize/villa |
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| ### 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 |
| ``` |
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| Next you can run the model as follows: |
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| ```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 |
| <!-- ## Training Details |
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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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| #### Hardware |
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| ## Glossary [optional] |
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