Update README of SQR-DETR (#278)

* update readme

* update sqr readme

* update sqr readme

* fixed sqr readme

README.md

@@ -133,6 +133,7 @@ Results and models are available in [model zoo](https://detrex.readthedocs.io/en
 - [x] [H-Deformable-DETR (CVPR'2023)](./projects/h_deformable_detr/)
 - [x] [MaskDINO (CVPR'2023)](./projects/maskdino/)
 - [x] [CO-MOT (ArXiv'2023)](./projects/co_mot/)
+- [x] [SQR-DETR (CVPR'2023)](./projects/sqr_detr/)
 
 Please see [projects](./projects/) for the details about projects that are built based on detrex.
 
@@ -246,6 +247,15 @@ If you use this toolbox in your research or wish to refer to the baseline result
  journal={arXiv preprint arXiv:2305.12724},
  year={2023}
 }
+
+@InProceedings{Chen_2023_CVPR,
+    author    = {Chen, Fangyi and Zhang, Han and Hu, Kai and Huang, Yu-Kai and Zhu, Chenchen and Savvides, Marios},
+    title     = {Enhanced Training of Query-Based Object Detection via Selective Query Recollection},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2023},
+    pages     = {23756-23765}
+}
 ```
 
 

projects/README.md

@@ -12,4 +12,5 @@ Here are projects that are built on detrex which show you use detrex as a librar
 - [Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment (ArXiv'2022)](./group_detr/)
 - [DETRs with Hybrid Matching (CVPR'2023)](./h_deformable_detr/)
 - [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation (CVPR'2023)](./maskdino/)
-- [NMS strikes back (ArXiv'2022)](./deta/)
+- [NMS strikes back (ArXiv'2022)](./deta/)
+- [Enhanced Training of Query-Based Object Detection via Selective Query Recollection (CVPR'2023)](./sqr_detr/)

projects/sqr_detr/README.md

@@ -0,0 +1,64 @@
+## Enhanced Training of Query-Based Object Detection via Selective Query Recollection
+
+Chen, Fangyi and Zhang, Han and Hu, Kai and Huang, Yu-Kai and Zhu, Chenchen and Savvides, Marios
+
+[[`arXiv`](https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Enhanced_Training_of_Query-Based_Object_Detection_via_Selective_Query_Recollection_CVPR_2023_paper.pdf)] [[`BibTeX`](#citing-sqr-detr)]
+
+<div align="center">
+  <img src="./assets/sqr_detr_overall.png"/>
+</div><br/>
+
+## Pretrained Models
+Here we provide the pretrained `SQR-DAB-DETR` weights based on detrex. 
+
+An MMDet implementation of SQR-Adamixer and SQR-Deformable-DETR can be found [here](https://github.com/Fangyi-Chen/SQR).
+
+<table><tbody>
+<!-- START TABLE -->
+<!-- TABLE HEADER -->
+<th valign="bottom">Name</th>
+<th valign="bottom">Backbone</th>
+<th valign="bottom">Pretrain</th>
+<th valign="bottom">Epochs</th>
+<th valign="bottom">box<br/>AP</th>
+<th valign="bottom">download</th>
+<!-- TABLE BODY -->
+<!-- ROW: dab_detr_r50_50ep -->
+ <tr><td align="left"><a href="configs/dab_detr_r50_50ep_sqr.py">SQR-DAB-DETR</a></td>
+<td align="center">R-50</td>
+<td align="center">IN1k</td>
+<td align="center">50</td>
+<td align="center">44.4</td>
+<td align="center"> <a href="https://drive.google.com/file/d/1Zz298GPYvDfF1RjSTk1e_ZoBy3NtXxWd/view?usp=sharingh">model</a></td>
+</tr>
+</tbody></table>
+
+
+## Training
+It can be trained with:
+```bash
+cd detrex
+python tools/train_net.py --config-file projects/sqr_detr/configs/path/to/config.py --num-gpus 8
+```
+By default, we use 8 GPUs with total batch size as 16 for training.
+
+## Evaluation
+Model evaluation can be done as follows:
+```bash
+cd detrex
+python tools/train_net.py --config-file projects/sqr_detr/configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint
+```
+
+## Citing SQR-DETR
+If you find our work helpful for your research, please consider citing the following BibTeX entry.
+
+```BibTex
+@InProceedings{Chen_2023_CVPR,
+    author    = {Chen, Fangyi and Zhang, Han and Hu, Kai and Huang, Yu-Kai and Zhu, Chenchen and Savvides, Marios},
+    title     = {Enhanced Training of Query-Based Object Detection via Selective Query Recollection},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2023},
+    pages     = {23756-23765}
+}
+```