Skip to content

add rtdetr obj365, test=document_fix #8567

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Aug 23, 2023
Merged

add rtdetr obj365, test=document_fix #8567

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions configs/rtdetr/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
- 发布RT-DETR-R34模型
- 发布RT-DETR-R18模型
- 发布RT-DETR-Swin和RT-DETR-FocalNet模型
- 发布RTDETR Obj365预训练模型

## 简介
<!-- We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge. Specifically, we design an efficient hybrid encoder to efficiently process multi-scale features by decoupling the intra-scale interaction and cross-scale fusion, and propose IoU-aware query selection to improve the initialization of object queries. In addition, our proposed detector supports flexibly adjustment of the inference speed by using different decoder layers without the need for retraining, which facilitates the practical application of real-time object detectors. Our RT-DETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RT-DETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR-R50 by 2.2% AP in accuracy and by about 21 times in FPS. -->
Expand Down Expand Up @@ -41,6 +42,8 @@ RT-DETR是第一个实时端到端目标检测器。具体而言，我们设计
## Objects365预训练模型
| Model | Epoch | Dataset | Input shape | $AP^{val}$ | $AP^{val}_{50}$ | T4 TensorRT FP16(FPS) | Weight | Logs
|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|:---:|
RT-DETR-R18 | 1x | Objects365 | 640 | 22.9 | 31.2 | - | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_1x_objects365.pdparams) | [log](https://github.com/lyuwenyu/RT-DETR/issues/8)
RT-DETR-R18 | 5x | COCO + Objects365 | 640 | 49.2 | 66.6 | 217 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_5x_coco_objects365.pdparams) | [log](https://github.com/lyuwenyu/RT-DETR/issues/8)
RT-DETR-R50 | 1x | Objects365 | 640 | 35.1 | 46.2 | - | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_1x_objects365.pdparams) | [log](https://github.com/lyuwenyu/RT-DETR/issues/8)
RT-DETR-R50 | 2x | COCO + Objects365 | 640 | 55.3 | 73.4 | 108 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_2x_coco_objects365.pdparams) | [log](https://github.com/lyuwenyu/RT-DETR/issues/8)
RT-DETR-R101 | 1x | Objects365 | 640 | 36.8 | 48.3 | - | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_1x_objects365.pdparams) | [log](https://github.com/lyuwenyu/RT-DETR/issues/8)
Expand Down