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[Docs] Normalize config README.md. (#7051)
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* regularize README.md

* Check README.md. Check backbone metafile

* Check backbone metafile

* Add metafile for some algorithm

* Change pre-commit-hooks

Change pre-commit-hooks to open-mmlab

Co-authored-by: Zaida Zhou <[email protected]>

* Add new metafile in model-index.yml

Co-authored-by: Zaida Zhou <[email protected]>
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24 changes: 10 additions & 14 deletions configs/albu_example/README.md
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# Albu Example

## Abstract
> [Albumentations: fast and flexible image augmentations](https://arxiv.org/abs/1809.06839)
<!-- [ABSTRACT] -->
<!-- [OTHERS] -->

## Abstract

Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve output labels. In computer vision domain, image augmentations have become a common implicit regularization technique to combat overfitting in deep convolutional neural networks and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations and combinations of flipping, rotating, scaling, and cropping. Moreover, the image processing speed varies in existing tools for image augmentation. We present Albumentations, a fast and flexible library for image augmentations with many various image transform operations available, that is also an easy-to-use wrapper around other augmentation libraries. We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations.

<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143870703-74f3ea3f-ae23-4035-9856-746bc3f88464.png" height="400" />
</div>

<!-- [PAPER_TITLE: Albumentations: fast and flexible image augmentations] -->
<!-- [PAPER_URL: https://arxiv.org/abs/1809.06839] -->
## Results and Models

## Citation
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) |

<!-- [OTHERS] -->
## Citation

```
```latex
@article{2018arXiv180906839B,
author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin},
title = "{Albumentations: fast and flexible image augmentations}",
Expand All @@ -27,9 +29,3 @@ Data augmentation is a commonly used technique for increasing both the size and
year = 2018
}
```

## Results and Models

| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) |
26 changes: 11 additions & 15 deletions configs/atss/README.md
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# Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
# ATSS

## Abstract
> [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://arxiv.org/abs/1912.02424)
<!-- [ABSTRACT] -->
<!-- [ALGORITHM] -->

## Abstract

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead.

<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143870776-c81168f5-e8b2-44ee-978b-509e4372c5c9.png"/>
</div>

<!-- [PAPER_TITLE: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection] -->
<!-- [PAPER_URL: https://arxiv.org/abs/1912.02424] -->
## Results and Models

## Citation
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) |
| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) |

<!-- [ALGORITHM] -->
## Citation

```latex
@article{zhang2019bridging,
Expand All @@ -26,10 +29,3 @@ Object detection has been dominated by anchor-based detectors for several years.
year = {2019}
}
```

## Results and Models

| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) |
| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) |
36 changes: 16 additions & 20 deletions configs/autoassign/README.md
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# AutoAssign: Differentiable Label Assignment for Dense Object Detection
# AutoAssign

## Abstract
> [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496)
<!-- [ALGORITHM] -->

<!-- [ABSTRACT] -->
## Abstract

Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions. To adapt to object appearances, Confidence Weighting is proposed to adjust the specific assign strategy of each instance. The two weighting modules are then combined to generate positive and negative weights to adjust each location's confidence. Extensive experiments on the MS COCO show that our method steadily surpasses other best sampling strategies by large margins with various backbones. Moreover, our best model achieves 52.1% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, e.g., PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign.

<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143870875-33567e44-0584-4470-9a90-0df0fb6c1fe2.png"/>
</div>

<!-- [PAPER_TITLE: AutoAssign: Differentiable Label Assignment for Dense Object Detection] -->
<!-- [PAPER_URL: https://arxiv.org/abs/2007.03496] -->

## Citation

<!-- [ALGORITHM] -->

```
@article{zhu2020autoassign,
title={AutoAssign: Differentiable Label Assignment for Dense Object Detection},
author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2007.03496},
year={2020}
}
```

## Results and Models

| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download |
Expand All @@ -37,3 +22,14 @@ Determining positive/negative samples for object detection is known as label ass

1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation.
2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch.

## Citation

```latex
@article{zhu2020autoassign,
title={AutoAssign: Differentiable Label Assignment for Dense Object Detection},
author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian},
journal={arXiv preprint arXiv:2007.03496},
year={2020}
}
```
42 changes: 19 additions & 23 deletions configs/carafe/README.md
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# CARAFE: Content-Aware ReAssembly of FEatures
# CARAFE

## Abstract
> [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188)
<!-- [ABSTRACT] -->
<!-- [ALGORITHM] -->

## Abstract

Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research.

<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143872016-48225685-0e59-49cf-bd65-a50ee04ca8a2.png"/>
</div>

<!-- [PAPER_TITLE: CARAFE: Content-Aware ReAssembly of FEatures] -->
<!-- [PAPER_URL: https://arxiv.org/abs/1905.02188] -->

## Citation

<!-- [ALGORITHM] -->

We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188).

```
@inproceedings{Wang_2019_ICCV,
title = {CARAFE: Content-Aware ReAssembly of FEatures},
author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```

## Results and Models

The results on COCO 2017 val is shown in the below table.
Expand All @@ -44,3 +26,17 @@ The results on COCO 2017 val is shown in the below table.
## Implementation

The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE.

## Citation

We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188).

```latex
@inproceedings{Wang_2019_ICCV,
title = {CARAFE: Content-Aware ReAssembly of FEatures},
author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```
48 changes: 22 additions & 26 deletions configs/cascade_rcnn/README.md
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# Cascade R-CNN: High Quality Object Detection and Instance Segmentation
# Cascade R-CNN

## Abstract
> [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://arxiv.org/abs/1906.09756)
<!-- [ABSTRACT] -->
<!-- [ALGORITHM] -->

## Abstract

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN.

<!-- [IMAGE] -->
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143872197-d99b90e4-4f05-4329-80a4-327ac862a051.png"/>
</div>

<!-- [PAPER_TITLE: Cascade R-CNN: High Quality Object Detection and Instance Segmentation] -->
<!-- [PAPER_URL: https://arxiv.org/abs/1906.09756] -->

## Citation

<!-- [ALGORITHM] -->

```latex
@article{Cai_2019,
title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/tpami.2019.2956516},
DOI={10.1109/tpami.2019.2956516},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Cai, Zhaowei and Vasconcelos, Nuno},
year={2019},
pages={1–1}
}
```

## Results and models
## Results and Models

### Cascade R-CNN

Expand Down Expand Up @@ -81,3 +61,19 @@ We also train some models with longer schedules and multi-scale training for Cas
| X-101-32x4d-FPN | pytorch| 3x | 9.0 | | 46.3 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234.log.json)
| X-101-32x8d-FPN | pytorch| 3x | 12.1 | | 46.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640.log.json)
| X-101-64x4d-FPN | pytorch| 3x | 12.0 | | 46.6 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311.log.json)

## Citation

```latex
@article{Cai_2019,
title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/tpami.2019.2956516},
DOI={10.1109/tpami.2019.2956516},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Cai, Zhaowei and Vasconcelos, Nuno},
year={2019},
pages={1–1}
}
```
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