We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159).
```
@inproceedings{
zhu2021deformable,
title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=gZ9hCDWe6ke}
}
```
## Results and Models
| Backbone | Model | Lr schd | box AP | Config | Download |
2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP.
We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).
```BibTeX
@article{qiao2020detectors,
title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution},
author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
journal={arXiv preprint arXiv:2006.02334},
year={2020}
}
```
## Dataset
DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
The directory should be like this.
```none
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
| | ├── stuffthingmaps
```
## Results and Models
DetectoRS includes two major components:
- Recursive Feature Pyramid (RFP).
- Switchable Atrous Convolution (SAC).
They can be used independently.
Combining them together results in DetectoRS.
The results on COCO 2017 val are shown in the below table.
| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
