Voxel51/cholect50

CholecT50 Dataset (FiftyOne Format)

This is a FiftyOne dataset version of the CholecT50 dataset.

CholecT50 is a dataset of laparoscopic cholecystectomy surgeries, annotated with surgical action triplets. It is the first public dataset to provide action triplet annotations for surgical videos, enabling research in fine-grained surgical activity recognition.

Dataset Description

• —Repository: CAMMA-public/cholect50
• —Paper: Rendition of CholecT50: A dataset for surgical action triplet recognition
• —Point of Contact: Chinedu Innocent Nwoye

Dataset Summary

CholecT50 consists of 50 videos of laparoscopic cholecystectomy surgeries. Each frame is annotated with action triplets of the form <Instrument, Verb, Target>.

• —Instruments: 7 categories (e.g., grasper, bipolar, hook)
• —Verbs: 10 categories (e.g., grasp, retract, cauterize)
• —Targets: 15 categories (e.g., gallbladder, cystic duct, liver)

The dataset contains approximately 100,000 annotated frames, providing a rich source for training and evaluating models for surgical video understanding.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

You can load the dataset from the Hugging Face Hub using FiftyOne:

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/cholect50")

# Launch the App
session = fo.launch_app(dataset)

Dataset Structure

The dataset is organized as a FiftyOne dataset. Each sample represents a video frame and includes the following fields:

• —filepath: The path to the frame image.
• —video_id: The ID of the video the frame belongs to.
• —frame_number: The frame number within the video.
• —instruments: Classifications for the instruments present in the frame.
• —verbs: Classifications for the verbs (actions) being performed.
• —targets: Classifications for the targets of the actions.
• —triplets: The full action triplet annotations.
• —clip_embeddings: 512-dimensional embeddings generated using a CLIP model (ViT-B/32).

Citation

If you use the CholecT50 dataset in your research, please cite the following paper:

@article{nwoye2022rendition,
  title={Rendition of CholecT50: A dataset for surgical action triplet recognition},
  author={Nwoye, Chinedu Innocent and Yu, Tong and Zahid, Anwar and Al Hajj, Hassan and Mutschler, Cristopher and Padoy, Nicolas},
  journal={arXiv preprint arXiv:2202.05324},
  year={2022}
}

License

The CholecT50 dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.