Merge branch 'main' of hf.co:datasets/chengyenhsieh/TAO-Amodal

README.md

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+# 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 all the annotations.
+```bash
+git lfs install
+git clone [email protected]: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
+```
+
+
+
+
+## 📚 Dataset Structure
+
+The dataset should be structured like this:
+```bash
+    ├── 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/annotations) 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}
+}
+```
+
+---
+task_categories:
+- object-detection
+- multi-object-tracking
+license: mit
+---