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--- |
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task_categories: |
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- object-detection |
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license: mit |
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tags: |
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- computer vision |
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- amodal-tracking |
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- object-tracking |
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- amodal-perception |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "amodal_annotations/train.json" |
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- split: validation |
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path: "amodal_annotations/validation.json" |
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- split: test |
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path: "amodal_annotations/test.json" |
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extra_gated_prompt: "To download the AVA and HACS videos you have to agree to terms and conditions." |
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extra_gated_fields: |
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You will use the Datasets only for non-commercial research and educational purposes.: |
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type: select |
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options: |
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- Yes |
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- No |
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You will NOT distribute the Datasets or any parts thereof.: |
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type: select |
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options: |
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- Yes |
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- No |
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Carnegie Mellon University makes no representations or warranties regarding the datasets, including but not limited to warranties of non-infringement or fitness for a particular purpose.: |
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type: select |
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options: |
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- Yes |
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- No |
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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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type: select |
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options: |
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- Yes |
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- No |
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You will treat people appearing in this data with respect and dignity.: |
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type: select |
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options: |
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- Yes |
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- No |
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This data comes with no warranty or guarantee of any kind, and you accept full liability.: |
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type: select |
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options: |
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- Yes |
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- No |
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extra_gated_heading: "TAO-Amodal VIDEO Request" |
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extra_gated_button_content: "Request Data" |
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--- |
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# TAO-Amodal Dataset |
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<!-- Provide a quick summary of the dataset. --> |
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Official Source for Downloading the TAO-Amodal Dataset. |
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[**📙 Project Page**](https://tao-amodal.github.io/) | [**💻 Code**](https://github.com/WesleyHsieh0806/TAO-Amodal) | [**📎 Paper Link**](https://arxiv.org/abs/2312.12433) | [**✏️ Citations**](#citations) |
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<div align="center"> |
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<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> |
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</div> |
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</br> |
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Contact: [🙋🏻♂️Cheng-Yen (Wesley) Hsieh](https://wesleyhsieh0806.github.io/) |
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## Dataset Description |
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Our dataset augments the TAO dataset with amodal bounding box annotations for fully invisible, out-of-frame, and occluded objects. |
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Note that this implies TAO-Amodal also includes modal segmentation masks (as visualized in the color overlays above). |
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Our dataset encompasses 880 categories, aimed at assessing the occlusion reasoning capabilities of current trackers |
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through the paradigm of Tracking Any Object with Amodal perception (TAO-Amodal). |
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### Dataset Download |
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1. Download with git: |
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```bash |
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git lfs install |
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git clone git@hf.co:datasets/chengyenhsieh/TAO-Amodal |
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``` |
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- Download with [`python`](https://huggingface.co/docs/huggingface_hub/guides/download#download-files-from-the-hub): |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download(repo_id="chengyenhsieh/TAO-Amodal") |
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``` |
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2. Download all the video frames: |
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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 |
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```bash |
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bash download_frames.sh |
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``` |
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## 📚 Dataset Structure |
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The dataset should be structured like this: |
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```bash |
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TAO-Amodal |
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├── frames |
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│ └── train |
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│ ├── ArgoVerse |
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│ ├── BDD |
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│ ├── Charades |
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│ ├── HACS |
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│ ├── LaSOT |
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│ └── YFCC100M |
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├── amodal_annotations |
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│ ├── train/validation/test.json |
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│ ├── train_lvis_v1.json |
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│ └── validation_lvis_v1.json |
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├── example_output |
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│ └── prediction.json |
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├── BURST_annotations |
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│ ├── train |
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│ └── train_visibility.json |
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│ ... |
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``` |
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## 📚 File Descriptions |
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| File Name | Description | |
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| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| 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. | |
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| 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. | |
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| 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. | |
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| 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). | |
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| 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 | |
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### Annotation and Prediction Format |
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Our annotations are structured similarly as [TAO](https://github.com/TAO-Dataset/tao/blob/master/tao/toolkit/tao/tao.py#L4) with some modifications. |
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Annotations: |
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```bash |
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Annotation file format: |
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{ |
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"info" : info, |
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"images" : [image], |
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"videos": [video], |
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"tracks": [track], |
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"annotations" : [annotation], |
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"categories": [category], |
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"licenses" : [license], |
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} |
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annotation: { |
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"id": int, |
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"image_id": int, |
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"track_id": int, |
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"bbox": [x,y,width,height], |
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"area": float, |
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# Redundant field for compatibility with COCO scripts |
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"category_id": int, |
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"video_id": int, |
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# Other important attributes for evaluation on TAO-Amodal |
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"amodal_bbox": [x,y,width,height], |
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"amodal_is_uncertain": bool, |
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"visibility": float, (0.~1.0) |
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} |
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image, info, video, track, category, licenses, : Same as TAO |
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``` |
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Predictions should be structured as: |
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```bash |
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[{ |
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"image_id" : int, |
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"category_id" : int, |
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"bbox" : [x,y,width,height], |
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"score" : float, |
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"track_id": int, |
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"video_id": int |
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}] |
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``` |
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Refer to the instructions of [TAO dataset](https://github.com/TAO-Dataset/tao/blob/master/docs/evaluation.md) for further details |
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## 📺 Example Sequences |
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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. |
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[<img src="https://tao-amodal.github.io/static/images/car_and_bus.png" width="50%">](https://tao-amodal.github.io/dataset.html "tao-amodal") |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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``` |
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@misc{hsieh2023tracking, |
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title={Tracking Any Object Amodally}, |
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author={Cheng-Yen Hsieh and Tarasha Khurana and Achal Dave and Deva Ramanan}, |
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year={2023}, |
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eprint={2312.12433}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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<details> |
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<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> |
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``` |
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@inproceedings{dave2020tao, |
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title={Tao: A large-scale benchmark for tracking any object}, |
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author={Dave, Achal and Khurana, Tarasha and Tokmakov, Pavel and Schmid, Cordelia and Ramanan, Deva}, |
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booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part V 16}, |
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pages={436--454}, |
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year={2020}, |
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organization={Springer} |
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} |
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@inproceedings{athar2023burst, |
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title={Burst: A benchmark for unifying object recognition, segmentation and tracking in video}, |
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author={Athar, Ali and Luiten, Jonathon and Voigtlaender, Paul and Khurana, Tarasha and Dave, Achal and Leibe, Bastian and Ramanan, Deva}, |
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booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, |
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pages={1674--1683}, |
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year={2023} |
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} |
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``` |
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</details> |
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