Update readme

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,