Update README.md
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README.md
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README.md
@ -23,10 +23,10 @@ Try these tracking modes for yourself with our [Colab demo](https://colab.resear
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# Installation Instructions
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## Installation Instructions
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Ensure you have both PyTorch and TorchVision installed on your system. Follow the instructions [here](https://pytorch.org/get-started/locally/) for the installation. We strongly recommend installing both PyTorch and TorchVision with CUDA support.
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## Pretrained models via PyTorch Hub
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### Pretrained models via PyTorch Hub
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The easiest way to use CoTracker is to load a pretrained model from torch.hub:
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```
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pip install einops timm tqdm
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@ -40,7 +40,7 @@ import tqdm
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cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker_w8")
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```
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Another option is to install it from this gihub repo. That's the best way if you need to run our demo or evaluate / train CoTracker:
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## Steps to Install CoTracker and its dependencies:
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### Steps to Install CoTracker and its dependencies:
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```
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git clone https://github.com/facebookresearch/co-tracker
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cd co-tracker
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@ -49,7 +49,7 @@ pip install opencv-python einops timm matplotlib moviepy flow_vis
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```
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## Download Model Weights:
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### Download Model Weights:
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```
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mkdir checkpoints
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cd checkpoints
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@ -60,13 +60,13 @@ cd ..
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```
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# Running the Demo:
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## Running the Demo:
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Try our [Colab demo](https://colab.research.google.com/github/facebookresearch/co-tracker/blob/master/notebooks/demo.ipynb) or run a local demo with 10*10 points sampled on a grid on the first frame of a video:
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```
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python demo.py --grid_size 10
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```
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# Evaluation
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## Evaluation
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To reproduce the results presented in the paper, download the following datasets:
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- [TAP-Vid](https://github.com/deepmind/tapnet)
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- [BADJA](https://github.com/benjiebob/BADJA)
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@ -82,7 +82,7 @@ python ./cotracker/evaluation/evaluate.py --config-name eval_badja exp_dir=./eva
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```
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By default, evaluation will be slow since it is done for one target point at a time, which ensures robustness and fairness, as described in the paper.
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# Training
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## Training
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To train the CoTracker as described in our paper, you first need to generate annotations for [Google Kubric](https://github.com/google-research/kubric) MOVI-f dataset. Instructions for annotation generation can be found [here](https://github.com/deepmind/tapnet).
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Once you have the annotated dataset, you need to make sure you followed the steps for evaluation setup and install the training dependencies:
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@ -99,13 +99,13 @@ python train.py --batch_size 1 --num_workers 28 \
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--save_every_n_epoch 10 --evaluate_every_n_epoch 10 --model_stride 4
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```
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# License
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## License
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The majority of CoTracker is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Particle Video Revisited is licensed under the MIT license, TAP-Vid is licensed under the Apache 2.0 license.
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# Acknowledgments
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## Acknowledgments
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We would like to thank [PIPs](https://github.com/aharley/pips) and [TAP-Vid](https://github.com/deepmind/tapnet) for publicly releasing their code and data. We also want to thank [Luke Melas-Kyriazi](https://lukemelas.github.io/) for proofreading the paper, [Jianyuan Wang](https://jytime.github.io/), [Roman Shapovalov](https://shapovalov.ro/) and [Adam W. Harley](https://adamharley.com/) for the insightful discussions.
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# Citing CoTracker
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## Citing CoTracker
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If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
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```
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@article{karaev2023cotracker,
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@ -114,4 +114,4 @@ If you find our repository useful, please consider giving it a star ⭐ and citi
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journal={arXiv:2307.07635},
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year={2023}
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}
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```
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```
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