metadata
library_name: transformers
tags: []
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
- 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 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