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2 changes: 1 addition & 1 deletion configs/albu_example/README.md
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# Albu Example

[OTHERS]
<!-- [OTHERS] -->

```
@article{2018arXiv180906839B,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{zhang2019bridging,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@article{zhu2020autoassign,
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## Introduction

[ALGORITHM]
<!-- [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).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{Cai_2019,
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# Cascade RPN

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@InProceedings{Dong_2020_CVPR,
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# Cityscapes Dataset

[DATASET]
<!-- [DATASET] -->

```
@inproceedings{Cordts2016Cityscapes,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{law2018cornernet,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```none
@inproceedings{dai2017deformable,
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}
```

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@article{zhu2018deformable,
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# DeepFashion

[DATASET]
<!-- [DATASET] -->

[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module
based on the dataset
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{wu2019rethinking,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@article{DynamicRCNN,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{zhu2019empirical,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{girshick2015fast,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{Ren_2017,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{tian2019fcos,
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# FoveaBox: Beyond Anchor-based Object Detector

[ALGORITHM]
<!-- [ALGORITHM] -->

FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797):
Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
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## Introduction

[OTHERS]
<!-- [OTHERS] -->

```latex
@article{micikevicius2017mixed,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{zhang2019freeanchor,
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# Feature Selective Anchor-Free Module for Single-Shot Object Detection

[ALGORITHM]
<!-- [ALGORITHM] -->

FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)).
Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level.
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks.

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388)

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@inproceedings{li2019gradient,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@article{weightstandardization,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{wu2018group,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{lu2019grid,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

This paper is motivated by the need to overcome to the limitations of existing
RoI extractors which select only one (the best) layer from FPN.
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{SunXLW19,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518).

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# InstaBoost for MMDetection

[ALGORITHM]
<!-- [ALGORITHM] -->

Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801).

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@Article{zheng2021LD,
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# Legacy Configs in MMDetection V1.x

[OTHERS]
<!-- [OTHERS] -->

Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos.

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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf).

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## Introduction

[DATASET]
<!-- [DATASET] -->

```latex
@inproceedings{gupta2019lvis,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{He_2017,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@inproceedings{huang2019msrcnn,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@article{wang2019fcos,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{ghiasi2019fpn,
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# Probabilistic Anchor Assignment with IoU Prediction for Object Detection

[ALGORITHM]
<!-- [ALGORITHM] -->

```latex
@inproceedings{paa-eccv2020,
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## Introduction

[ALGORITHM]
<!-- [ALGORITHM] -->

```
@inproceedings{liu2018path,
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