amodal annotations for visualization, training, and evaluation

README.md

@@ -1,147 +1,3 @@
-# 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
----
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