README.md

---
task_categories:
- object-detection
license: mit
tags:
- computer vision
- amodal-tracking
- object-tracking
- amodal-perception
configs:
- config_name: default
  data_files:
  - split: train
    path: "amodal_annotations/train.json"
  - split: validation
    path: "amodal_annotations/validation.json"
  - split: test
    path: "amodal_annotations/test.json"
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  You accept full responsibility for your use of the datasets and shall defend and indemnify Carnegie Mellon University, including its employees, officers and agents, against any and all claims arising from your use of the datasets, including but not limited to your use of any copyrighted videos or images that you may create from the datasets.:
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extra_gated_heading: "TAO-Amodal VIDEO Request"
extra_gated_button_content: "Request Data"
---

# TAO-Amodal Dataset

<!-- Provide a quick summary of the dataset. -->
 Official Source for Downloading the TAO-Amodal Dataset.
   
   [**📙 Project Page**](https://tao-amodal.github.io/)  | [**💻 Code**](https://github.com/WesleyHsieh0806/TAO-Amodal) | [**📎 Paper Link**](https://arxiv.org/abs/2312.12433) | [**✏️ Citations**](#citations)
   
   <div align="center">
  <a href="https://tao-amodal.github.io/"><img width="95%" alt="TAO-Amodal" src="https://tao-amodal.github.io/static/images/webpage_preview.png"></a>
   </div>

</br>

Contact: [🙋🏻‍♂️Cheng-Yen (Wesley) Hsieh](https://wesleyhsieh0806.github.io/)

## Dataset Description
Our dataset augments the TAO dataset with amodal bounding box annotations for fully invisible, out-of-frame, and occluded objects. 
Note that this implies TAO-Amodal also includes modal segmentation masks (as visualized in the color overlays above). 
Our dataset encompasses 880 categories, aimed at assessing the occlusion reasoning capabilities of current trackers 
through the paradigm of Tracking Any Object with Amodal perception (TAO-Amodal).

### Dataset Download
1. Download with git:
```bash
git lfs install
git clone [email protected]:datasets/chengyenhsieh/TAO-Amodal
```

- Download with [`python`](https://huggingface.co/docs/huggingface_hub/guides/download#download-files-from-the-hub):

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="chengyenhsieh/TAO-Amodal")
```

2. Download all the video frames:

You can either download the frames following the instructions [here](https://motchallenge.net/tao_download.php) (recommended) or modify our provided [script](./download_frames.sh) and run
```bash
bash download_frames.sh
```



## 📚 Dataset Structure

The dataset should be structured like this:
```bash
   TAO-Amodal
    ├── frames
    │    └── train
    │       ├── ArgoVerse
    │       ├── BDD
    │       ├── Charades
    │       ├── HACS
    │       ├── LaSOT
    │       └── YFCC100M
    ├── amodal_annotations
    │    ├── train/validation/test.json
    │    ├── train_lvis_v1.json
    │    └── validation_lvis_v1.json
    ├── example_output
    │    └── prediction.json
    ├── BURST_annotations
    │    ├── train
    │         └── train_visibility.json
    │    ...

```

## 📚 File Descriptions

| File Name                  | Description                                                                                                                                                                                                                             |
| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| train/validation/test.json | Formal annotation files. We use these annotations for visualization. Categories include those in [lvis](https://www.lvisdataset.org/) v0.5 and freeform categories.                                                                     |
| train_lvis_v1.json         | We use this file to train our [amodal-expander](https://tao-amodal.github.io/index.html#Amodal-Expander), treating each image frame as an independent sequence. Categories are aligned with those in lvis v1.0.                         |
| validation_lvis_v1.json    | We use this file to evaluate our [amodal-expander](https://tao-amodal.github.io/index.html#Amodal-Expander). Categories are aligned with those in lvis v1.0.                                                                            |
| prediction.json            | Example output json from amodal-expander. Tracker predictions should be structured like this file to be evaluated with our [evaluation toolkit](https://github.com/WesleyHsieh0806/TAO-Amodal?tab=readme-ov-file#bar_chart-evaluation). |
| BURST_annotations/XXX.json | Modal mask annotations from [BURST dataset](https://github.com/Ali2500/BURST-benchmark) with our heuristic visibility attributes. We provide these files for the convenience of visualization                                           |

### Annotation and Prediction Format

Our annotations are structured similarly as [TAO](https://github.com/TAO-Dataset/tao/blob/master/tao/toolkit/tao/tao.py#L4) with some modifications.
Annotations:
```bash

Annotation file format:
{
    "info" : info,
    "images" : [image],
    "videos": [video],
    "tracks": [track],
    "annotations" : [annotation],
    "categories": [category],
    "licenses" : [license],
}
annotation: {
    "id": int,
    "image_id": int,
    "track_id": int,
    "bbox": [x,y,width,height],
    "area": float,

    # Redundant field for compatibility with COCO scripts
    "category_id": int,
    "video_id": int,

    # Other important attributes for evaluation on TAO-Amodal
    "amodal_bbox": [x,y,width,height],
    "amodal_is_uncertain": bool,
    "visibility": float, (0.~1.0)
}
image, info, video, track, category, licenses, : Same as TAO
```

Predictions should be structured as:

```bash
[{
    "image_id" : int,
    "category_id" : int,
    "bbox" : [x,y,width,height],
    "score" : float,
    "track_id": int,
    "video_id": int
}]
```
Refer to the instructions of [TAO dataset](https://github.com/TAO-Dataset/tao/blob/master/docs/evaluation.md) for further details


## 📺 Example Sequences
Check [here](https://tao-amodal.github.io/#TAO-Amodal) for more examples and [here](https://github.com/WesleyHsieh0806/TAO-Amodal?tab=readme-ov-file#artist-visualization) for visualization code.
[<img src="https://tao-amodal.github.io/static/images/car_and_bus.png" width="50%">](https://tao-amodal.github.io/dataset.html "tao-amodal")



## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@misc{hsieh2023tracking,
    title={Tracking Any Object Amodally},
    author={Cheng-Yen Hsieh and Tarasha Khurana and Achal Dave and Deva Ramanan},
    year={2023},
    eprint={2312.12433},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

<details>
  <summary>Please also cite <a href="https://taodataset.org/">TAO</a> and <a href="https://github.com/Ali2500/BURST-benchmark">BURST</a> dataset if you use our dataset</summary>

  ```
@inproceedings{dave2020tao,
    title={Tao: A large-scale benchmark for tracking any object},
    author={Dave, Achal and Khurana, Tarasha and Tokmakov, Pavel and Schmid, Cordelia and Ramanan, Deva},
    booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part V 16},
    pages={436--454},
    year={2020},
    organization={Springer}
  }

@inproceedings{athar2023burst,
  title={Burst: A benchmark for unifying object recognition, segmentation and tracking in video},
  author={Athar, Ali and Luiten, Jonathon and Voigtlaender, Paul and Khurana, Tarasha and Dave, Achal and Leibe, Bastian and Ramanan, Deva},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1674--1683},
  year={2023}
}
  ```

</details>