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Nikita Karaev
2023-07-21 15:57:03 +01:00
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@@ -23,10 +23,10 @@ Try these tracking modes for yourself with our [Colab demo](https://colab.resear
# Installation Instructions ## Installation Instructions
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. 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.
## Pretrained models via PyTorch Hub ### Pretrained models via PyTorch Hub
The easiest way to use CoTracker is to load a pretrained model from torch.hub: The easiest way to use CoTracker is to load a pretrained model from torch.hub:
``` ```
pip install einops timm tqdm pip install einops timm tqdm
@@ -40,7 +40,7 @@ import tqdm
cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker_w8") cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker_w8")
``` ```
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: 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:
## Steps to Install CoTracker and its dependencies: ### Steps to Install CoTracker and its dependencies:
``` ```
git clone https://github.com/facebookresearch/co-tracker git clone https://github.com/facebookresearch/co-tracker
cd co-tracker cd co-tracker
@@ -49,7 +49,7 @@ pip install opencv-python einops timm matplotlib moviepy flow_vis
``` ```
## Download Model Weights: ### Download Model Weights:
``` ```
mkdir checkpoints mkdir checkpoints
cd checkpoints cd checkpoints
@@ -60,13 +60,13 @@ cd ..
``` ```
# Running the Demo: ## Running the Demo:
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: 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:
``` ```
python demo.py --grid_size 10 python demo.py --grid_size 10
``` ```
# Evaluation ## Evaluation
To reproduce the results presented in the paper, download the following datasets: To reproduce the results presented in the paper, download the following datasets:
- [TAP-Vid](https://github.com/deepmind/tapnet) - [TAP-Vid](https://github.com/deepmind/tapnet)
- [BADJA](https://github.com/benjiebob/BADJA) - [BADJA](https://github.com/benjiebob/BADJA)
@@ -82,7 +82,7 @@ python ./cotracker/evaluation/evaluate.py --config-name eval_badja exp_dir=./eva
``` ```
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. 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.
# Training ## Training
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). 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).
Once you have the annotated dataset, you need to make sure you followed the steps for evaluation setup and install the training dependencies: Once you have the annotated dataset, you need to make sure you followed the steps for evaluation setup and install the training dependencies:
@@ -99,13 +99,13 @@ python train.py --batch_size 1 --num_workers 28 \
--save_every_n_epoch 10 --evaluate_every_n_epoch 10 --model_stride 4 --save_every_n_epoch 10 --evaluate_every_n_epoch 10 --model_stride 4
``` ```
# License ## License
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. 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.
# Acknowledgments ## Acknowledgments
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. 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.
# Citing CoTracker ## Citing CoTracker
If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work: If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
``` ```
@article{karaev2023cotracker, @article{karaev2023cotracker,