This repository is an official implementation of the InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions.

Paper | Blog in Chinese

• Feb 28, 2023: InternImage is accepted to CVPR 2023!
• Nov 18, 2022: 🚀 InternImage-XL merged into BEVFormer v2 achieves state-of-the-art performance of 63.4 NDS on nuScenes Camera Only.
• Nov 10, 2022: 🚀🚀 InternImage-H achieves a new record 65.4 mAP on COCO detection test-dev and 62.9 mIoU on ADE20K, outperforming previous models by a large margin.

• Other downstream tasks.
• TensorRT inference.
• Classification code of the InternImage series.
• InternImage-T/S/B/L/XL ImageNet-1k pretrained model.
• InternImage-L/XL ImageNet-22k pretrained model.
• InternImage-T/S/B/L/XL detection and instance segmentation model.
• InternImage-T/S/B/L/XL semantic segmentation model.

InternImage, initially described in arxiv, can be a general backbone for computer vision. It takes deformable convolution as the core operator to obtain large effective receptive fields, and introducing adaptive spatial aggregation to reduces the strict inductive bias. Our model makes it possible to learn more stronger and robust models with large-scale parameters from massive data.

ImageNet-1K and ImageNet-22K Pretrained InternImage Models

COCO Object Detection

ADE20K Semantic Segmentation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@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},
  journal={arXiv preprint arXiv:2211.05778},
  year={2022}
}