diff --git a/README.md b/README.md
index 269edc44c..f361a3e6d 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,13 @@ COCO panoptic val5k models:
 Checkout our [panoptic colab](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/DETR_panoptic.ipynb)
 to see how to use and visualize DETR's panoptic segmentation prediction.
 
+# Notebooks
+
+We provide a few notebooks in colab to help you get a grasp on DETR:
+* [DETR's hands on Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb): Shows how to load a model from hub, generate predictions, then visualize the attention of the model (similar to the figures of the paper)
+* [Standalone Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_demo.ipynb): In this notebook, we demonstrate how to implement a simplified version of DETR from the grounds up in 50 lines of Python, then visualize the predictions. It is a good starting point if you want to gain better understanding the architecture and poke around before diving in the codebase.
+* [Panoptic Colab Notebook](https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/DETR_panoptic.ipynb): Demonstrates how to use DETR for panoptic segmentation and plot the predictions.
+
 
 # Usage - Object detection
 There are no extra compiled components in DETR and package dependencies are minimal,