Model Card for timesformer_GP_scroll1

The grandprize winning model of the Vesuvius Challenge of 2023.

Model Details

Model Description

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

How to Get Started with the Model

Make sure to have the dependencies installed, namely transformers and Timesformer package

pip install -U transformers timesformer-pytorch

Next you can run the model as follows:

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

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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Collection including scrollprize/timesformer_GP_scroll1