Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- image-segmentation
+- vision
+datasets:
+- coco
+---
+
+# DETRs with Collaborative Hybrid Assignments Training
+
+## Introduction
+
+In this paper, we present a novel collaborative hybrid assignments training scheme, namely Co-DETR, to learn more efficient and effective DETR-based detectors from versatile label assignment manners. 
+1. **Encoder optimization**: The proposed training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training multiple parallel auxiliary heads supervised by one-to-many label assignments. 
+2. **Decoder optimization**: We conduct extra customized positive queries by extracting the positive coordinates from these auxiliary heads to improve attention learning of the decoder. 
+3. **State-of-the-art performance**: Co-DETR with ViT-Large (304M parameters) is **the first model to achieve 66.0 AP on COCO test-dev.**
+
+## Model Zoo
+
+| Model | Backbone | Aug | Dataset | box AP (val) | mask AP (val) |
+| --- | --- | --- | --- | --- | --- |
+| Co-DETR | ViT-L | LSJ	| LVIS | 67.3 | 60.7 |
+
+## How to use
+
+We implement Co-DETR using [MMDetection V2.25.3](https://github.com/open-mmlab/mmdetection/releases/tag/v2.25.3) and [MMCV V1.5.0](https://github.com/open-mmlab/mmcv/releases/tag/v1.5.0). Please refer to our [github repo](https://github.com/Sense-X/Co-DETR/tree/main) for more details.
+
+### Training
+Train Co-Deformable-DETR + ResNet-50 with 8 GPUs:
+```shell
+sh tools/dist_train.sh projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py 8 path_to_exp
+```
+Train using slurm:
+```shell
+sh tools/slurm_train.sh partition job_name projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_exp
+```
+
+### Testing
+Test Co-Deformable-DETR + ResNet-50 with 8 GPUs, and evaluate:
+```shell
+sh tools/dist_test.sh  projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_checkpoint 8 --eval bbox
+```
+Test using slurm:
+```shell
+sh tools/slurm_test.sh partition job_name projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_checkpoint --eval bbox
+```
+
+## Cite Co-DETR
+
+If you find this repository useful, please use the following BibTeX entry for citation.
+
+```latex
+@inproceedings{zong2023detrs,
+  title={Detrs with collaborative hybrid assignments training},
+  author={Zong, Zhuofan and Song, Guanglu and Liu, Yu},
+  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+  pages={6748--6758},
+  year={2023}
+}
+```