---
license: mit
task_categories:
  - image-to-image
library_name:
  - datasets
tags:
  - point-tracking
  - optical-flow
  - video
  - dense-correspondence
---

# AllTracker: Efficient Dense Point Tracking Dataset

This repository contains the data produced/postprocessed as part of [**AllTracker: Efficient Dense Point Tracking at High Resolution**](https://huggingface.co/papers/2506.07310).

AllTracker is a model that estimates long-range point tracks by estimating the flow field between a query frame and every other frame of a video. This dataset supports the training and evaluation of such models, providing high-resolution and dense correspondence fields.

**Project Page:** [https://alltracker.github.io](https://alltracker.github.io)
**GitHub Repository (Code):** [https://github.com/aharley/alltracker/](https://github.com/aharley/alltracker/)
**Hugging Face Model Page:** [https://huggingface.co/aharley/alltracker](https://huggingface.co/aharley/alltracker)
**Gradio Demo:** [https://huggingface.co/spaces/aharley/alltracker](https://huggingface.co/spaces/aharley/alltracker)

## Dataset Usage and Preparation

This data is used by the training scripts in the associated [GitHub repository](https://github.com/aharley/alltracker/). For detailed instructions on how to download, prepare, and use this dataset for training, please refer to the [**"Data prep" section in the GitHub repository's README**](https://github.com/aharley/alltracker/#data-prep).

## Citation

If you use this dataset or the associated code for your research, please cite the paper:

```bibtex
@inproceedings{harley2025alltracker,
author    = {Adam W. Harley and Yang You and Xinglong Sun and Yang Zheng and Nikhil Raghuraman and Yunqi Gu and Sheldon Liang and Wen-Hsuan Chu and Achal Dave and Pavel Tokmakov and Suya You and Rares Ambrus and Katerina Fragkiadaki and Leonidas J. Guibas},
title     = {All{T}racker: {E}fficient Dense Point Tracking at High Resolution},
booktitle = {ICCV},
year      = {2025}
}
```