README.md updates

README.md

@@ -1,14 +1,21 @@
+# Grounding DINO 
+
+---
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)
+
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=grounding-dino-marrying-dino-with-grounded)
 
-# Grounding DINO 
+
 
 Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now!
 
 
 ## Highlight
+
 - **Open-Set Detection.** Detect **everything** with language!
 - **High Performancce.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.
 - **Flexible.** Collaboration with Stable Diffusion for Image Editting.
@@ -23,21 +30,22 @@ Description
 <img src=".asset/hero_figure.png" alt="ODinW" width="100%">
 </details>
 
-## TODO List
+## TODO 
 
 - [x] Release inference code and demo.
 - [x] Release checkpoints.
 - [ ] Grounding DINO with Stable Diffusion and GLIGEN demos.
 
+## Install 
 
-## Usage
-### 1. Install 
 If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set.
+
 ```bash
 pip install -e .
 ```
 
-### 2. Run an inference demo 
+## Demo
+
 See the `demo/inference_on_a_image.py` for more details.
 ```bash
 CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
@@ -48,7 +56,8 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
   -t "cat ear."
 ```
 
-### Checkpoints
+## Checkpoints
+
 <!-- insert a table -->
 <table>
   <thead>
@@ -74,6 +83,7 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
 </table>
 
 ## Results
+
 <details open>
 <summary><font size="4">
 COCO Object Detection Results
@@ -102,11 +112,6 @@ Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</
 <img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%">
 </details>
 
-
-
-
-
-
 ## Model
 
 Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
@@ -114,7 +119,8 @@ Includes: a text backbone, an image backbone, a feature enhancer, a language-gui
 ![arch](.asset/arch.png)
 
 
-# Links
+## Acknowledgement
+
 Our model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/GLIP). Thanks for their great work!
 
 We also thank great previous work including DETR, Deformable DETR, SMCA, Conditional DETR, Anchor DETR, Dynamic DETR, DAB-DETR, DN-DETR, etc. More related work are available at [Awesome Detection Transformer](https://github.com/IDEACVR/awesome-detection-transformer). A new toolbox [detrex](https://github.com/IDEA-Research/detrex) is available as well.
@@ -122,8 +128,10 @@ We also thank great previous work including DETR, Deformable DETR, SMCA, Conditi
 Thanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models.
 
 
-# Bibtex
+## Citation
+
 If you find our work helpful for your research, please consider citing the following BibTeX entry.   
+
 ```bibtex
 @inproceedings{ShilongLiu2023GroundingDM,
   title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection},