From b0eca4e2b8ee0aa28ca06b074f7eddee4929c4e9 Mon Sep 17 00:00:00 2001 From: alcinos Date: Sun, 28 Jun 2020 18:39:54 +0200 Subject: [PATCH] Add new colab link in README (#114) --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) 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,