From f25f9c58e3f5530bfa766d07a8e3db2a0453dea7 Mon Sep 17 00:00:00 2001 From: nikitakaraevv Date: Fri, 21 Jul 2023 07:55:39 -0700 Subject: [PATCH] Update readme --- README.md | 38 +++++++++++++++++++++++++++----------- 1 file changed, 27 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 5a43e8d..0f218b9 100644 --- a/README.md +++ b/README.md @@ -23,15 +23,29 @@ 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. +## Pretrained models via PyTorch Hub +The easiest way to use CoTracker is to load a pretrained model from torch.hub: +``` +pip install einops timm tqdm +``` +``` +import torch +import timm +import einops +import tqdm + +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: ## Steps to Install CoTracker and its dependencies: ``` git clone https://github.com/facebookresearch/co-tracker cd co-tracker pip install -e . -pip install opencv-python einops timm matplotlib moviepy flow_vis +pip install opencv-python einops timm matplotlib moviepy flow_vis ``` @@ -46,13 +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: ``` python demo.py --grid_size 10 ``` -## Evaluation +# Evaluation To reproduce the results presented in the paper, download the following datasets: - [TAP-Vid](https://github.com/deepmind/tapnet) - [BADJA](https://github.com/benjiebob/BADJA) @@ -60,36 +74,38 @@ To reproduce the results presented in the paper, download the following datasets And install the necessary dependencies: ``` -pip install hydra-core==1.1.0 mediapy tensorboard +pip install hydra-core==1.1.0 mediapy ``` Then, execute the following command to evaluate on BADJA: ``` python ./cotracker/evaluation/evaluate.py --config-name eval_badja exp_dir=./eval_outputs dataset_root=your/badja/path ``` +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). Once you have the annotated dataset, you need to make sure you followed the steps for evaluation setup and install the training dependencies: ``` -pip install pytorch_lightning==1.6.0 +pip install pytorch_lightning==1.6.0 tensorboard ``` Now you can launch training on Kubric. Our model was trained for 50000 iterations on 32 GPUs (4 nodes with 8 GPUs). +Modify *dataset_root* and *ckpt_path* accordingly before running this command: ``` python train.py --batch_size 1 --num_workers 28 \ ---num_steps 50000 --ckpt_path ./ --model_name cotracker \ +--num_steps 50000 --ckpt_path ./ --dataset_root ./datasets --model_name cotracker \ --save_freq 200 --sequence_len 24 --eval_datasets tapvid_davis_first badja \ --traj_per_sample 256 --sliding_window_len 8 --updateformer_space_depth 6 --updateformer_time_depth 6 \ --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. -## 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. -## Citing CoTracker +# Citing CoTracker If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work: ``` @article{karaev2023cotracker,