diff --git a/README.md b/README.md
index 0f218b9..4a1b024 100644
--- a/README.md
+++ b/README.md
@@ -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.
 
-## 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:
 ```
 pip install einops timm tqdm
@@ -40,7 +40,7 @@ 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:
+### Steps to Install CoTracker and its dependencies:
 ```
 git clone https://github.com/facebookresearch/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
 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:
 ```
 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)
@@ -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.
 
-# 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:
@@ -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
 ```
 
-# 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,
@@ -114,4 +114,4 @@ If you find our repository useful, please consider giving it a star ⭐ and citi
   journal={arXiv:2307.07635},
   year={2023}
 }
-```
\ No newline at end of file
+```