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Update README (#94) 

* support ema for main_deepspeed.py, fix torch.distribute.launch

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<p>
<a href="./README_CN.md">[中文版本]</a>
</p>
There are a lot of issues now, our team will check and solve them one by one, please wait patiently.
We currently receive a bunch of issues, our team will check and solve them one by one, please stay tuned.
# INTERN-2.5: Multimodal Multitask General Large Model
......@@ -25,22 +25,23 @@ There are a lot of issues now, our team will check and solve them one by one, pl
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/image-classification-on-places205)](https://paperswithcode.com/sota/image-classification-on-places205?p=internimage-exploring-large-scale-vision)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/image-classification-on-imagenet)](https://paperswithcode.com/sota/image-classification-on-imagenet?p=internimage-exploring-large-scale-vision)
This repository is an official implementation of the [InternImage: Exploring Large-Scale Vision Foundation Models with
Deformable Convolutions](https://arxiv.org/abs/2211.05778).
The official implementation of
[Paper](https://arxiv.org/abs/2211.05778) \| [Blog in Chinese](https://zhuanlan.zhihu.com/p/610772005) | [Documents](./docs/)
[InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://arxiv.org/abs/2211.05778).
[[Paper](https://arxiv.org/abs/2211.05778)] [[Blog in Chinese](https://zhuanlan.zhihu.com/p/610772005)] [[Documents](./docs/)]
## Introduction
SenseTime and Shanghai AI Laboratory jointly released the multimodal multitask general model "INTERN-2.5" on March 14, 2023. "INTERN-2.5" achieved multiple breakthroughs in multimodal multitask processing, and its excellent cross-modal task processing ability in text and image can provide efficient and accurate perception and understanding capabilities for general scenarios such as autonomous driving.
## Overview
### Overview
<div align=left>
<img src='./docs/figs/intern_pipeline_en.png' width=900>
</div>
## Highlights
### Highlights
- :thumbsup: **The strongest visual universal backbone model with up to 3 billion parameters**
- 🏆 **Achieved `90.1% Top1` accuracy in ImageNet, the most accurate among open-source models**
- 🏆 **Achieved `65.5 mAP` on the COCO benchmark dataset for object detection, the only model that exceeded `65.0 mAP`**
......@@ -55,16 +56,18 @@ ADE20K, outperforming previous models by a large margin.
## Applications
### 1. Performance on Image Modality Tasks
- On the ImageNet benchmark dataset,
"INTERN-2.5" achieved a Top-1 accuracy of 90.1% using only publicly available data for image classification. This is the only model, besides two undisclosed models from Google and Microsoft and additional datasets, to achieve a Top-1 accuracy of over 90.0%. It is also the highest-accuracy open-source model on ImageNet and the largest model in scale in the world.
- On the COCO object detection benchmark dataset, "INTERN-2.5" achieved a mAP of 65.5, making it the only model in the world to surpass 65 mAP.
- "INTERN-2.5" achieved the world's best performance on 16 other important visual benchmark datasets, covering classification, detection, and segmentation tasks.
### 🌅 Image Modality Tasks
"INTERN-2.5" achieved an impressive Top-1 accuracy of 90.1% on the ImageNet benchmark dataset using only publicly available data for image classification. Apart from two undisclosed models trained with additional datasets by Google and Microsoft, "INTERN-2.5" is the only open-source model that achieves a Top-1 accuracy of over 90.0%, and it is also the largest model in scale worldwide.
"INTERN-2.5" outperformed all other models worldwide on the COCO object detection benchmark dataset with a remarkable mAP of 65.5, making it the only model that surpasses 65 mAP in the world.
"INTERN-2.5" also demonstrated world's best performance on 16 other important visual benchmark datasets, covering a wide range of tasks such as classification, detection, and segmentation, making it the top-performing model across multiple domains.
<div align="left">
<br>
**Classification Task**
**Performance**
- Classification
<table border="1" width="90%">
<tr align="center">
<th colspan="1"> Image Classification</th><th colspan="2"> Scene Classification </th><th colspan="1">Long-Tail Classification</th>
......@@ -76,10 +79,9 @@ ADE20K, outperforming previous models by a large margin.
<th>90.1</th><th>61.2</th><th>71.7</th><th>92.3</th>
</tr>
</table>
<br>
- Detection
**Detection Task**
<table border="1" width="90%">
<tr align="center">
<th colspan="4"> Conventional Object Detection</th><th colspan="3">Long-Tail Object Detection </th><th colspan="1">Autonomous Driving Object Detection</th><th colspan="1">Dense Object Detection</th>
......@@ -91,9 +93,8 @@ ADE20K, outperforming previous models by a large margin.
<th>65.5</th><th>94.0</th><th>97.2</th><th>74.1</th><th>65.8</th><th>63.2</th><th>38.8</th><th>64.8</th><th>97.2</th>
</tr>
</table>
<br>
**Segmentation Task**
- Segmentation
<table border="1" width="90%">
<tr align="center">
<th colspan="3">Semantic Segmentation</th><th colspan="1">Street Segmentation</th><th colspan="1">RGBD Segmentation</th>
......@@ -107,25 +108,16 @@ ADE20K, outperforming previous models by a large margin.
</table>
<br>
</div>
### 🌁 📖 Image and Text Cross-Modal Tasks
### 2. Cross-Modal Performance for Image and Text Tasks
**Image-Text Retrieval**: "INTERN-2.5" can quickly locate and retrieve the most semantically relevant images based on textual content requirements. This capability can be applied to both videos and image collections and can be further combined with object detection boxes to enable a variety of applications, helping users quickly and easily find the required image resources. For example, it can return the relevant images specified by the text in the album.
- Image-Text Retrieval
"INTERN-2.5" can quickly locate and retrieve the most semantically relevant images based on textual content requirements. This capability can be applied to both videos and image collections and can be further combined with object detection boxes to enable a variety of applications, helping users quickly and easily find the required image resources. For example, it can return the relevant images specified by the text in the album.
**Image-To-Text**: "INTERN-2.5" has a strong understanding capability in various aspects of visual-to-text tasks such as image captioning, visual question answering, visual reasoning, and optical character recognition. For example, in the context of autonomous driving, it can enhance the scene perception and understanding capabilities, assist the vehicle in judging traffic signal status, road signs, and other information, and provide effective perception information support for vehicle decision-making and planning.
- Image-To-Text
**Performance**
"INTERN-2.5" has a strong understanding capability in various aspects of visual-to-text tasks such as image captioning, visual question answering, visual reasoning, and optical character recognition. For example, in the context of autonomous driving, it can enhance the scene perception and understanding capabilities, assist the vehicle in judging traffic signal status, road signs, and other information, and provide effective perception information support for vehicle decision-making and planning.
<div align="left">
<br>
**Multimodal Tasks**
<table border="1" width="90%">
<tr align="center">
<th colspan="1">Image Captioning</th><th colspan="2">Fine-tuning Image-Text Retrieval</th><th colspan="1">Zero-shot Image-Text Retrieval</th>
......@@ -139,8 +131,6 @@ ADE20K, outperforming previous models by a large margin.
</table>
<br>
</div>
## Core Technologies
The outstanding performance of "INTERN-2.5" in the field of cross-modal learning is due to several innovations in the core technology of multi-modal multi-task general model, including the development of InternImage as the backbone network for visual perception, LLM as the large-scale text pre-training network for text processing, and Uni-Perceiver as the compatible decoding modeling for multi-task learning.
......@@ -151,37 +141,28 @@ InternImage, the visual backbone network of "INTERN-2.5", has a parameter size o
<img src='./docs/figs/network.png' width=900>
</div>
## Released Models
## Project Release
- [ ] Model for other downstream tasks
- [x] Integration of [Deepspeed](https://github.com/microsoft/DeepSpeed) for classification.
- [x] InternImage-H(1B)/G(3B)
- [x] TensorRT inference
- [x] Classification code of the InternImage series
- [x] InternImage-T/S/B/L/XL ImageNet-1K pretrained model
- [x] InternImage-L/XL ImageNet-22K pretrained model
- [x] InternImage-T/S/B/L/XL detection and instance segmentation model
- [x] InternImage-T/S/B/L/XL semantic segmentation model
## Related Projects
- Object Detection and Instance Segmentation: [COCO](detection/configs/coco/)
- Semantic Segmentation: [ADE20K](segmentation/configs/ade20k/), [Cityscapes](segmentation/configs/cityscapes/)
- Image-Text Retrieval, Image Captioning, and Visual Question Answering: [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver)
- 3D Perception: [BEVFormer](https://github.com/fundamentalvision/BEVFormer)
<details>
<summary> Open-source Visual Pretrained Models </summary>
<br>
<div>
## Open-source Visual Pretrained Models
| name | pretrain | pre-training resolution | #param | download |
| :------------: | :----------: | :----------------------: | :----: | :---------------------------------------------------------------------------------------------------: |
| InternImage-L | ImageNet-22K | 384x384 | 223M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth) |
| InternImage-XL | ImageNet-22K | 384x384 | 335M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22k_192to384.pth) |
| InternImage-H | Joint 427M | 384x384 | 1.08B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_jointto22k_384.pth) |
| InternImage-G | - | 384x384 | 3B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_pretrainto22k_384.pth) |
</div>
</details>
<details>
<summary> ImageNet-1K Image Classification </summary>
<br>
<div>
## ImageNet-1K Image Classification
| name | pretrain | resolution | acc@1 | #param | FLOPs | download |
| :------------: | :----------: | :--------: | :---: | :----: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| InternImage-T | ImageNet-1K | 224x224 | 83.5 | 30M | 5G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth) \| [cfg](classification/configs/without_lr_decay/internimage_t_1k_224.yaml) |
......@@ -191,9 +172,14 @@ InternImage, the visual backbone network of "INTERN-2.5", has a parameter size o
| InternImage-XL | ImageNet-22K | 384x384 | 88.0 | 335M | 163G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22kto1k_384.pth) \| [cfg](classification/configs/without_lr_decay/internimage_xl_22kto1k_384.yaml) |
| InternImage-H | Joint 427M | 640x640 | 89.6 | 1.08B | 1478G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_22kto1k_640.pth) \| [cfg](classification/configs/without_lr_decay/internimage_h_22kto1k_640.yaml) |
| InternImage-G | - | 512x512 | 90.1 | 3B | 2700G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_22kto1k_512.pth) \| [cfg](classification/configs/without_lr_decay/internimage_g_22kto1k_512.yaml) |
</div>
</details>
## COCO Object Detection and Instance Segmentation
<details>
<summary> COCO Object Detection and Instance Segmentation </summary>
<br>
<div>
| backbone | method | schd | box mAP | mask mAP | #param | FLOPs | download |
| :------------: | :--------: | :---: | :-----: | :------: | :----: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
......@@ -213,9 +199,16 @@ InternImage, the visual backbone network of "INTERN-2.5", has a parameter size o
| InternImage-H | DINO (TTA) | 65.0 / 65.4 | 2.18B | TODO | TODO |
| InternImage-G | DINO (TTA) | 65.3 / 65.5 | 3B | TODO | TODO |
## ADE20K Semantic Segmentation
</div>
</details>
<details>
<summary> ADE20K Semantic Segmentation </summary>
<br>
<div>
| backbone | method | resolution | mIoU (ss/ms) | #param | FLOPs | download |
| :------------: | :---------: | :--------: | :----------: | :----: | :---: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| InternImage-T | UperNet | 512x512 | 47.9 / 48.1 | 59M | 944G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_t_512_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_t_512_160k_ade20k.py) |
......@@ -226,14 +219,20 @@ InternImage, the visual backbone network of "INTERN-2.5", has a parameter size o
| InternImage-H | UperNet | 896x896 | 59.9 / 60.3 | 1.12B | 3566G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_h_896_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_h_896_160k_ade20k.py) |
| InternImage-H | Mask2Former | 896x896 | 62.5 / 62.9 | 1.31B | 4635G | TODO |
</div>
</details>
## Main Results of FPS
<details>
<summary> Main Results of FPS </summary>
<br>
<div>
[export classification model from pytorch to tensorrt](classification/README.md#export)
[Export classification model from pytorch to tensorrt](classification/README.md#export)
[export detection model from pytorch to tensorrt](detection/README.md#export)
[Export detection model from pytorch to tensorrt](detection/README.md#export)
[export segmentation model from pytorch to tensorrt](segmentation/README.md#export)
[Export segmentation model from pytorch to tensorrt](segmentation/README.md#export)
| name | resolution | #param | FLOPs | batch 1 FPS (TensorRT) |
| :------------: | :--------: | :----: | :---: | :--------------------: |
......@@ -262,13 +261,35 @@ pip install -e .
```
For more details on building custom ops, please refering to [this document](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/01-how-to-build/linux-x86_64.md).
</div>
</details>
## Citation
## History
- [ ] Model for other downstream tasks
- [x] Integration of [Deepspeed](https://github.com/microsoft/DeepSpeed) for classification.
- [x] InternImage-H(1B)/G(3B)
- [x] TensorRT inference
- [x] Classification code of the InternImage series
- [x] InternImage-T/S/B/L/XL ImageNet-1K pretrained model
- [x] InternImage-L/XL ImageNet-22K pretrained model
- [x] InternImage-T/S/B/L/XL detection and instance segmentation model
- [x] InternImage-T/S/B/L/XL semantic segmentation model
## Related Projects
- Object Detection and Instance Segmentation: [COCO](detection/configs/coco/)
- Semantic Segmentation: [ADE20K](segmentation/configs/ade20k/), [Cityscapes](segmentation/configs/cityscapes/)
- Image-Text Retrieval, Image Captioning, and Visual Question Answering: [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver)
- 3D Perception: [BEVFormer](https://github.com/fundamentalvision/BEVFormer)
## Citations
If this work is helpful for your research, please consider citing the following BibTeX entry.
```
```bibtex
@article{wang2022internimage,
title={InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions},
author={Wang, Wenhai and Dai, Jifeng and Chen, Zhe and Huang, Zhenhang and Li, Zhiqi and Zhu, Xizhou and Hu, Xiaowei and Lu, Tong and Lu, Lewei and Li, Hongsheng and others},
......
README_CN.md
View file @ f0d93d15
......@@ -27,18 +27,18 @@
这个代码仓库是[InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://arxiv.org/abs/2211.05778)的官方实现。
[论文](https://arxiv.org/abs/2211.05778) \| [知乎专栏](https://zhuanlan.zhihu.com/p/610772005) | [文档](./docs/)
[[论文](https://arxiv.org/abs/2211.05778)] [[知乎专栏](https://zhuanlan.zhihu.com/p/610772005)] [[文档](./docs/)]
## 简介
商汤科技与上海人工智能实验室在2023年3月14日联合发布多模态多任务通用大模型“书生2.5”。“书生2.5”在多模态多任务处理能力中斩获多项全新突破，其卓越的图文跨模态任务处理能力可为自动驾驶等通用场景任务提供高效精准的感知和理解能力支持。“书生2.5”致力于多模态多任务通用模型的构建，旨在接收处理各种不同模态的输入，并采用统一的模型架构和参数处理各种不同的任务，促进不同模态和任务之间在表示学习方面的协作，逐步实现通用人工智能领域的融会贯通。
## 概览图
### 概览图
<div align=left>
<img src='./docs/figs/intern_pipeline.png' width=900>
</div>
## 亮点
### 亮点
- :thumbsup: **高达30亿参数的最强视觉通用主干模型**
- 🏆 **图像分类标杆数据集ImageNet `90.1% Top1`准确率，开源模型中准确度最高**
- 🏆 **物体检测标杆数据集COCO `65.5 mAP`，唯一超过`65 mAP`的模型**
......@@ -148,35 +148,28 @@
</div>
## 项目功能
- [ ] 各类下游任务
- [x] DCNv3 预编译的whl包
- [x] InternImage-H(1B)/G(3B)
- [x] TensorRT 推理
- [x] InternImage 系列分类代码
- [x] InternImage-T/S/B/L/XL ImageNet-1K 预训练模型
- [x] InternImage-L/XL ImageNet-22K 预训练模型
- [x] InternImage-T/S/B/L/XL 检测和实例分割模型
- [x] InternImage-T/S/B/L/XL 语义分割模型
## 预训练模型
## 相关开源项目
- 目标检测和实例分割: [COCO](detection/configs/coco/)
- 语义分割: [ADE20K](segmentation/configs/ade20k/), [Cityscapes](segmentation/configs/cityscapes/)
- 图文检索、图像描述和视觉问答: [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver)
- 3D感知: [BEVFormer](https://github.com/fundamentalvision/BEVFormer)
<details>
<summary> 开源视觉预训练模型 </summary>
<br>
<div>
## 开源视觉预训练模型
| name | pretrain | pre-training resolution | #param | download |
| :------------: | :----------: | :----------------------: | :----: | :---------------------------------------------------------------------------------------------------: |
| InternImage-L | ImageNet-22K | 384x384 | 223M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth) |
| InternImage-XL | ImageNet-22K | 384x384 | 335M | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22k_192to384.pth) |
| InternImage-H | Joint 427M | 384x384 | 1.08B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_jointto22k_384.pth) |
| InternImage-G | - | 384x384 | 3B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_pretrainto22k_384.pth) |
</div>
</details>
<details>
<summary> ImageNet-1K图像分类 </summary>
<br>
<div>
## ImageNet-1K图像分类
| name | pretrain | resolution | acc@1 | #param | FLOPs | download |
| :------------: | :----------: | :--------: | :---: | :----: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| InternImage-T | ImageNet-1K | 224x224 | 83.5 | 30M | 5G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth) \| [cfg](classification/configs/without_lr_decay/internimage_t_1k_224.yaml) |
......@@ -186,10 +179,14 @@
| InternImage-XL | ImageNet-22K | 384x384 | 88.0 | 335M | 163G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22kto1k_384.pth) \| [cfg](classification/configs/without_lr_decay/internimage_xl_22kto1k_384.yaml) |
| InternImage-H | Joint 427M | 640x640 | 89.6 | 1.08B | 1478G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_22kto1k_640.pth) \| [cfg](classification/configs/without_lr_decay/internimage_h_22kto1k_640.yaml) |
| InternImage-G | - | 512x512 | 90.1 | 3B | 2700G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_22kto1k_512.pth) \| [cfg](classification/configs/without_lr_decay/internimage_g_22kto1k_512.yaml) |
</div>
</details>
## COCO目标检测和实例分割
<details>
<summary> COCO目标检测和实例分割 </summary>
<br>
<div>
| backbone | method | schd | box mAP | mask mAP | #param | FLOPs | download |
| :------------: | :--------: | :---: | :-----: | :------: | :----: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
......@@ -209,7 +206,15 @@
| InternImage-H | DINO (TTA) | 65.0 / 65.4 | 2.18B | TODO | TODO |
| InternImage-G | DINO (TTA) | 65.3 / 65.5 | 3B | TODO | TODO |
## ADE20K语义分割
</div>
</details>
<details>
<summary> ADE20K语义分割 </summary>
<br>
<div>
| backbone | method | resolution | mIoU (ss/ms) | #param | FLOPs | download |
| :------------: | :---------: | :--------: | :----------: | :----: | :---: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
......@@ -221,8 +226,14 @@
| InternImage-H | UperNet | 896x896 | 59.9 / 60.3 | 1.12B | 3566G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_h_896_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_h_896_160k_ade20k.py) |
| InternImage-H | Mask2Former | 896x896 | 62.5 / 62.9 | 1.31B | 4635G | TODO |
</div>
</details>
## 模型推理速度
<details>
<summary> 模型推理速度 </summary>
<br>
<div>
[export classification model from pytorch to tensorrt](classification/README.md#export)
......@@ -239,6 +250,7 @@
| InternImage-XL | 384x384 | 335M | 163G | 47 |
在使用`mmdeploy`将PyTorch模型转为TensorRT之前，请确保您已正确编译DCNv3的自定义算子，其安装方式如下：
```shell
export MMDEPLOY_DIR=/the/root/path/of/MMDeploy
......@@ -255,15 +267,38 @@ make -j$(nproc) && make install
cd ${MMDEPLOY_DIR}
pip install -e .
```
关于`mmdeploy`编译自定义算子的更多细节，请参考这份[文档](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/01-how-to-build/linux-x86_64.md)。
</div>
</details>
## 项目功能
- [ ] 各类下游任务
- [x] DCNv3 预编译的whl包
- [x] InternImage-H(1B)/G(3B)
- [x] TensorRT 推理
- [x] InternImage 系列分类代码
- [x] InternImage-T/S/B/L/XL ImageNet-1K 预训练模型
- [x] InternImage-L/XL ImageNet-22K 预训练模型
- [x] InternImage-T/S/B/L/XL 检测和实例分割模型
- [x] InternImage-T/S/B/L/XL 语义分割模型
## 相关开源项目
- 目标检测和实例分割: [COCO](detection/configs/coco/)
- 语义分割: [ADE20K](segmentation/configs/ade20k/), [Cityscapes](segmentation/configs/cityscapes/)
- 图文检索、图像描述和视觉问答: [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver)
- 3D感知: [BEVFormer](https://github.com/fundamentalvision/BEVFormer)
## 引用
若“书生2.5”对您的研究工作有帮助，请参考如下bibtex对我们的工作进行引用。
```
```bibtex
@article{wang2022internimage,
title={InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions},
author={Wang, Wenhai and Dai, Jifeng and Chen, Zhe and Huang, Zhenhang and Li, Zhiqi and Zhu, Xizhou and Hu, Xiaowei and Lu, Tong and Lu, Lewei and Li, Hongsheng and others},
......
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