Datasets:
videos
list | annotations
list | tracks
list | images
list | info
dict | categories
list | licenses
sequence |
---|---|---|---|---|---|---|
[{"id":0,"width":640,"height":480,"neg_category_ids":[342,57,651,357,738],"not_exhaustive_category_i(...TRUNCATED) | [{"bbox":[114.0,166.0,67.0,71.0],"area":4757.0,"iscrowd":0,"id":0,"image_id":0,"category_id":95,"tra(...TRUNCATED) | [{"id":0,"category_id":95,"video_id":0},{"id":1,"category_id":805,"video_id":0},{"id":2,"category_id(...TRUNCATED) | [{"id":0,"video":"train/YFCC100M/v_f69ebe5b731d3e87c1a3992ee39c3b7e","_scale_task_id":"5de800eddb2c1(...TRUNCATED) | {"year":2020,"version":"0.1.20200120","description":"Annotations imported from Scale","contributor":(...TRUNCATED) | [{"frequency":"r","id":1,"synset":"acorn.n.01","image_count":0,"instance_count":0,"synonyms":["acorn(...TRUNCATED) | [
"Unknown"
] |
TAO-Amodal Dataset
Official Source for Downloading the TAO-Amodal Dataset.
π Project Page | π» Code | π Paper Link | βοΈ Citations
Contact: ππ»ββοΈCheng-Yen (Wesley) Hsieh
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
- Download with git:
git lfs install
git clone git@hf.co:datasets/chengyenhsieh/TAO-Amodal
- Download with
python
:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="chengyenhsieh/TAO-Amodal")
- Unzip all videos:
Modify dataset_root
in unzip_video.py and run:
python unzip_video.py
π Dataset Structure
The dataset should be structured like this:
TAO-Amodal
βββ frames
β βββ train
β βββ ArgoVerse
β βββ BDD
β βββ Charades
β βββ HACS
β βββ LaSOT
β βββ YFCC100M
βββ amodal_annotations
β βββ train/validation/test.json
β βββ train_lvis_v1.json
β βββ validation_lvis_v1.json
βββ example_output
β βββ prediction.json
βββ BURST_annotations
β βββ train
β βββ train_visibility.json
β ...
π File Descriptions
File Name | Description |
---|---|
train/validation/test.json | Formal annotation files. We use these annotations for visualization. Categories include those in lvis v0.5 and freeform categories. |
train_lvis_v1.json | We use this file to train our 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. 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. |
BURST_annotations/XXX.json | Modal mask annotations from BURST dataset 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 with some modifications. Annotations:
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:
[{
"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 for further details
πΊ Example Sequences
Check here for more examples and here for visualization code.
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}
}
- Downloads last month
- 0