--- 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" --- # TAO-Amodal Dataset Official Source for Downloading the TAO-Amodal Dataset. [**πŸ“™ Project Page**](https://tao-amodal.github.io/) | [**πŸ’» Code**](https://github.com/WesleyHsieh0806/TAO-Amodal) | [**πŸ“Ž Paper Link**](https://arxiv.org/abs/2312.12433) | [**✏️ Citations**](#citations)
TAO-Amodal

Contact: [πŸ™‹πŸ»β€β™‚οΈ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 all the annotations. ```bash git lfs install git clone git@hf.co:datasets/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_TAO.sh) and run ```bash bash download_TAO.sh ``` ## πŸ“š Dataset Structure The dataset should be structured like this: ```bash TAO-Amodal β”œβ”€β”€ frames β”‚ └── train β”‚ β”œβ”€β”€ ArgoVerse β”‚ β”œβ”€β”€ BDD β”‚ β”œβ”€β”€ Charades β”‚ β”œβ”€β”€ HACS β”‚ β”œβ”€β”€ LaSOT β”‚ └── YFCC100M β”œβ”€β”€ amodal_annotations β”‚ β”œβ”€β”€ train/validation/test.json β”‚ β”œβ”€β”€ train_lvis_v1.json β”‚ └── validation_lvis_v1.json β”œβ”€β”€ example_output β”‚ └── prediction.json β”œβ”€β”€ BURST_annotations β”‚ β”œβ”€β”€ train β”‚ └── train_visibility.json β”‚ ... ``` ## πŸ“š 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 ## πŸ“Ί 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. [](https://tao-amodal.github.io/dataset.html "tao-amodal") ## Citation ``` @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} } ```
Please also cite TAO and BURST dataset if you use our dataset ``` @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} } ```