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Official Training and Inference Code of Amodal Expander, Proposed in Tracking Any Object Amodally

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Amodal Expander

Official Training and Inference Code of Amodal Expander, Proposed in Tracking Any Object Amodally.

📙 Project Page | :octocat: Official Github | 📎 Paper Link | ✏️ Citations


Amodal Expander serves as a plug-in module that can “amodalize” any ex-isting detector or tracker with limited (amodal) training data.

TAO-Amodal

📌 Leave a ⭐ in our official repository to keep track of the updates.


Table of Contents


🎒 Get Started

See installation instructions.

🏃 Training and Inference

We augment the SOTA modal tracker GTR with Amodal Expander by fine-tuning on TAO-Amodal dataset.

Please prepare datasets and check our MODEL ZOO for training/inference instructions.

📊 Evaluation

After obtaining the prediction JSON lvis_instances_results.json through the above inference pipeline. You can evaluate the tracker results using our evaluation toolkit.

🤖 Demo

You can test our model on a single video through:

python demo.py --config-file configs/GTR_TAO_Amodal_Expander_PasteNOcclude.yaml \
               --video-input demo/input_video.mp4 \
               --output      demo/output.mp4 \
               --opts        MODEL.WEIGHTS /path/to/Amodal_Expander_PnO_45k.pth

Use --input video_folder/*.jpg instead if the video consists of image frames.

🐇 PasteNOcclude

PasteNOcclude serves as a data augmentation technique to automatically generate more occlusion scenarios. Check the Jupyter demo and implementation details (link 1, link 2, link 3).

TAO-Amodal

Acknowledgement

This repository is built upon Global Tracking Transformer and Detectron2.

LICENSE

Check here for further details.

Citations

@article{hsieh2023tracking,
  title={Tracking any object amodally},
  author={Hsieh, Cheng-Yen and Khurana, Tarasha and Dave, Achal and Ramanan, Deva},
  journal={arXiv preprint arXiv:2312.12433},
  year={2023}
}

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Official Training and Inference Code of Amodal Expander, Proposed in Tracking Any Object Amodally

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