# InternImage **Repository Path**: kinkar/InternImage ## Basic Information - **Project Name**: InternImage - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-12-02 - **Last Updated**: 2025-12-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

[English Version]

# 书生图像 - 大规模视觉基础模型 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-lvis-v1-0-minival)](https://paperswithcode.com/sota/object-detection-on-lvis-v1-0-minival?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-lvis-v1-0-val)](https://paperswithcode.com/sota/object-detection-on-lvis-v1-0-val?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-pascal-voc-2012)](https://paperswithcode.com/sota/object-detection-on-pascal-voc-2012?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-openimages-v6)](https://paperswithcode.com/sota/object-detection-on-openimages-v6?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/object-detection-on-crowdhuman-full-body)](https://paperswithcode.com/sota/object-detection-on-crowdhuman-full-body?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/2d-object-detection-on-bdd100k-val)](https://paperswithcode.com/sota/2d-object-detection-on-bdd100k-val?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/semantic-segmentation-on-ade20k)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/semantic-segmentation-on-cityscapes)](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/semantic-segmentation-on-cityscapes-val)](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes-val?p=internimage-exploring-large-scale-vision) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/internimage-exploring-large-scale-vision/semantic-segmentation-on-pascal-context)](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?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-inaturalist-2018)](https://paperswithcode.com/sota/image-classification-on-inaturalist-2018?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-places365)](https://paperswithcode.com/sota/image-classification-on-places365?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-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/bevformer-v2-adapting-modern-image-backbones/3d-object-detection-on-nuscenes-camera-only)](https://paperswithcode.com/sota/3d-object-detection-on-nuscenes-camera-only?p=bevformer-v2-adapting-modern-image-backbones) [![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) 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。 \[[论文](https://arxiv.org/abs/2211.05778)\] \[[知乎专栏](https://zhuanlan.zhihu.com/p/610772005)\] ## 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** ## 最新进展 - 2024年1月22日: 🚀 在 InternImage 中支持了 [DCNv4](https://github.com/OpenGVLab/DCNv4)! - 2023年2月28日: 🚀 InternImage 被 CVPR 2023 接收! - 2022年11月18日: 🚀 基于 InternImage-XL 主干网络，[BEVFormer v2](https://arxiv.org/abs/2211.10439) 在nuScenes的纯视觉3D检测任务上取得了最佳性能 `63.4 NDS` ！ - 2022年11月10日: 🚀 InternImage-H 在 COCO 目标检测任务上以 `65.4 mAP` 斩获冠军，是唯一突破 `65.0 mAP` 的超强物体检测模型！ - 2022年11月10日: 🚀 InternImage-H 在 ADE20K 语义分割数据集上取得 `62.9 mIoU` 的SOTA性能！ ## 项目功能 - [x] 各类下游任务 - [x] 支持 [CVPR 2023 Workshop on End-to-End Autonomous Driving](https://opendrivelab.com/e2ead/cvpr23)，[详见](https://github.com/OpenGVLab/InternImage/tree/master/autonomous_driving) - [x] 支持提取模型中间层特征，[详见](classification/extract_feature.py) - [x] 支持基于 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 的低成本训练，[详见](https://github.com/OpenGVLab/InternImage/tree/master/classification) - [x] DCNv3 算子预编译 `.whl` 包，[详见](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files) - [x] InternImage-H(1B)/G(3B) - [x] 支持分类/检测/分割 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 ## 简介 InternImage 是一个由上海人工智能实验室、清华大学等机构的研究人员提出的基于卷积神经网络（CNN）的视觉基础模型。与基于 Transformer 的网络不同，InternImage 以可变形卷积 DCNv3 作为核心算子，使模型不仅具有检测和分割等下游任务所需的动态有效感受野，而且能够进行自适应的空间聚合。
与 InternImage 相关的其他项目还包括：预训练算法 M3I-Pretraining，通用解码器 Uni-Perceiver 系列，以及自动驾驶感知通用编码器 BEVFormer 系列。
## 性能 - 在图像分类标杆数据集 ImageNet 上，InternImage 仅基于公开数据便达到了 90.1% 的 Top-1 准确率。这是除谷歌与微软两个未公开模型及额外数据集外，唯一准确率超过 90.0% 的模型，同时也是世界上开源模型中 ImageNet 准确度最高，规模最大的模型； - 在物体检测标杆数据集 COCO 上，InternImage 取得了 65.5 的 mAP，是世界上唯一超过 65 mAP 的模型； - 在另外 16 个重要的视觉基础数据集（覆盖分类、检测和分割任务）上取得世界最好性能。 **分类任务**
图像分类 场景分类 长尾分类
ImageNetPlaces365Places 205iNaturalist 2018
90.161.271.792.6
**检测任务**
常规物体检测 长尾物体检测 自动驾驶物体检测 密集物体检测
COCOVOC 2007VOC 2012OpenImageLVIS minivalLVIS valBDD100KnuScenesCrowdHuman
65.594.097.274.165.863.238.864.897.2
**分割任务**
语义分割街景分割RGBD分割
ADE20KCOCO Stuff-10KPascal ContextCityScapesNYU Depth V2
62.959.670.387.068.1
## 已发布模型
开源视觉预训练模型
| name | pretrain | resolution | #param | download | | :------------: | :----------: | :--------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | InternImage-L | ImageNet-22K | 384x384 | 223M | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_l_22k_384) | | InternImage-XL | ImageNet-22K | 384x384 | 335M | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22k_192to384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_xl_22k_384) | | InternImage-H | Joint 427M | 384x384 | 1.08B | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_jointto22k_384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_h_jointto22k_384) | | InternImage-G | Joint 427M | 384x384 | 3B | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_pretrainto22k_384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_g_jointto22k_384) |
ImageNet-1K 图像分类
| name | pretrain | resolution | acc@1 | #param | FLOPs | download | | :------------: | :----------: | :--------: | :---: | :----: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | InternImage-T | ImageNet-1K | 224x224 | 83.5 | 30M | 5G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_t_1k_224.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_t_1k_224) \| [cfg](classification/configs/without_lr_decay/internimage_t_1k_224.yaml) | | InternImage-S | ImageNet-1K | 224x224 | 84.2 | 50M | 8G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_s_1k_224.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_s_1k_224) \| [cfg](classification/configs/without_lr_decay/internimage_s_1k_224.yaml) | | InternImage-B | ImageNet-1K | 224x224 | 84.9 | 97M | 16G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_b_1k_224.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_b_1k_224) \| [cfg](classification/configs/without_lr_decay/internimage_b_1k_224.yaml) | | InternImage-L | ImageNet-22K | 384x384 | 87.7 | 223M | 108G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22kto1k_384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_l_22kto1k_384) \| [cfg](classification/configs/without_lr_decay/internimage_l_22kto1k_384.yaml) | | InternImage-XL | ImageNet-22K | 384x384 | 88.0 | 335M | 163G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_xl_22kto1k_384.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_xl_22kto1k_384) \| [cfg](classification/configs/without_lr_decay/internimage_xl_22kto1k_384.yaml) | | InternImage-H | Joint 427M | 640x640 | 89.6 | 1.08B | 1478G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_h_22kto1k_640.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_h_22kto1k_640) \| [cfg](classification/configs/without_lr_decay/internimage_h_22kto1k_640.yaml) | | InternImage-G | Joint 427M | 512x512 | 90.1 | 3B | 2700G | [pth](https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_g_22kto1k_512.pth) \| [hf](https://huggingface.co/OpenGVLab/internimage_g_22kto1k_512) \| [cfg](classification/configs/without_lr_decay/internimage_g_22kto1k_512.yaml) |
COCO 目标检测和实例分割
| backbone | method | schd | box mAP | mask mAP | #param | FLOPs | download | | :------------: | :--------: | :--: | :-----: | :------: | :----: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | InternImage-T | Mask R-CNN | 1x | 47.2 | 42.5 | 49M | 270G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_1x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py) | | InternImage-T | Mask R-CNN | 3x | 49.1 | 43.7 | 49M | 270G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_3x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_t_fpn_3x_coco.py) | | InternImage-S | Mask R-CNN | 1x | 47.8 | 43.3 | 69M | 340G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_1x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_s_fpn_1x_coco.py) | | InternImage-S | Mask R-CNN | 3x | 49.7 | 44.5 | 69M | 340G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_3x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_s_fpn_3x_coco.py) | | InternImage-B | Mask R-CNN | 1x | 48.8 | 44.0 | 115M | 501G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_1x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_b_fpn_1x_coco.py) | | InternImage-B | Mask R-CNN | 3x | 50.3 | 44.8 | 115M | 501G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_3x_coco.pth) \| [cfg](detection/configs/coco/mask_rcnn_internimage_b_fpn_3x_coco.py) | | InternImage-L | Cascade | 1x | 54.9 | 47.7 | 277M | 1399G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_l_fpn_1x_coco.pth) \| [cfg](detection/configs/coco/cascade_internimage_l_fpn_1x_coco.py) | | InternImage-L | Cascade | 3x | 56.1 | 48.5 | 277M | 1399G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_l_fpn_3x_coco.pth) \| [cfg](detection/configs/coco/cascade_internimage_l_fpn_3x_coco.py) | | InternImage-XL | Cascade | 1x | 55.3 | 48.1 | 387M | 1782G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_1x_coco.pth) \| [cfg](detection/configs/coco/cascade_internimage_xl_fpn_1x_coco.py) | | InternImage-XL | Cascade | 3x | 56.2 | 48.8 | 387M | 1782G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_3x_coco.pth) \| [cfg](detection/configs/coco/cascade_internimage_xl_fpn_3x_coco.py) | | backbone | method | box mAP (val/test) | #param | download | | :--------------: | :--------: | :----------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | CB-InternImage-H | DINO (TTA) | 65.0 / 65.4 | 2.18B | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_cbinternimage_h_objects365_coco.pth) \| [cfg](https://github.com/OpenGVLab/InternImage/blob/master/detection/configs/coco/dino_4scale_cbinternimage_h_objects365_coco_ss.py) | | CB-InternImage-G | DINO (TTA) | 65.3 / 65.5 | 6B | TODO |
ADE20K 语义分割
| 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) | | InternImage-S | UperNet | 512x512 | 50.1 / 50.9 | 80M | 1017G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_s_512_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_s_512_160k_ade20k.py) | | InternImage-B | UperNet | 512x512 | 50.8 / 51.3 | 128M | 1185G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_b_512_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_b_512_160k_ade20k.py) | | InternImage-L | UperNet | 640x640 | 53.9 / 54.1 | 256M | 2526G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_l_640_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_l_640_160k_ade20k.py) | | InternImage-XL | UperNet | 640x640 | 55.0 / 55.3 | 368M | 3142G | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_xl_640_160k_ade20k.pth) \| [cfg](segmentation/configs/ade20k/upernet_internimage_xl_640_160k_ade20k.py) | | 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 | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_896_80k_cocostuff2ade20k.pth) \| [cfg](segmentation/configs/ade20k/mask2former_internimage_h_896_80k_cocostuff2ade20k_ss.py) |
模型推理速度
[Export classification model from pytorch to tensorrt](classification/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) | name | resolution | #param | FLOPs | batch 1 FPS (TensorRT) | | :------------: | :--------: | :----: | :---: | :--------------------: | | InternImage-T | 224x224 | 30M | 5G | 156 | | InternImage-S | 224x224 | 50M | 8G | 129 | | InternImage-B | 224x224 | 97M | 16G | 116 | | InternImage-L | 384x384 | 223M | 108G | 56 | | 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 # prepare our custom ops, you can find it at InternImage/tensorrt/modulated_deform_conv_v3 cp -r modulated_deform_conv_v3 ${MMDEPLOY_DIR}/csrc/mmdeploy/backend_ops/tensorrt # build custom ops cd ${MMDEPLOY_DIR} mkdir -p build && cd build cmake -DCMAKE_CXX_COMPILER=g++-7 -DMMDEPLOY_TARGET_BACKENDS=trt -DTENSORRT_DIR=${TENSORRT_DIR} -DCUDNN_DIR=${CUDNN_DIR} .. make -j$(nproc) && make install # install the mmdeploy after building custom ops 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)。
## 相关项目 ### 多模态基础模型 - [Uni-Perceiver](https://github.com/fundamentalvision/Uni-Perceiver): 通用感知任务预训练统一框架, 可直接处理 zero-shot 和 few-shot 任务 - [Uni-Perceiver v2](https://arxiv.org/abs/2211.09808): 用于处理图像/图文任务的通用模型 - [M3I-Pretraining](https://github.com/OpenGVLab/M3I-Pretraining): 基于最大化输入和目标的互信息的单阶段预训练范式 - [InternVL](https://github.com/OpenGVLab/InternVL): 领先的多模态大语言模型，在 OCR、多模态推理和对话等任务中表现卓越 ### 自动驾驶 - [BEVFormer](https://github.com/fundamentalvision/BEVFormer): 基于 BEV 的新一代纯视觉环视感知方案 - [BEVFormer v2](https://arxiv.org/abs/2211.10439): 融合 BEV 感知和透视图检测的两阶段检测器 ## 算法竞赛 - [2022 Waymo 3D Camera-Only Detection Challenge](https://waymo.com/open/challenges/2022/3d-camera-only-detection/): 基于 InternImage，BEVFormer++ 取得赛道冠军 - [nuScenes 3D detection](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera): BEVFormer v2 在 nuScenes 纯视觉检测任务中取得SOTA性能 (64.8 NDS) - [CVPR 2023 Workshop End-to-End Autonomous Driving](https://opendrivelab.com/e2ead/cvpr23): InternImage 作为 baseline 支持了比赛 [3D Occupancy Prediction Challenge](https://opendrivelab.com/AD23Challenge.html#Track3) 和 [OpenLane Topology Challenge](https://opendrivelab.com/AD23Challenge.html#Track1) ## 引用 若这个工作对您的研究有帮助，请参考如下 BibTeX 对我们的工作进行引用。 ```bibtex @inproceedings{wang2023internimage, 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}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={14408--14419}, year={2023} } ```