This repository is an official implementation of the InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions.
By Wenhai Wang, Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li, Xiaogang Wang, Yu Qiao
Nov 18, 2022: 🚀 InternImage-XL merged into BEVFormer v2 achieves state-of-the-art performance of63.4 NDSon nuScenes Camera Only.Nov 10, 2022: 🚀🚀 InternImage-H achieves a new record65.4 mAPon COCO detection test-dev and62.9 mIoUon ADE20K, outperforming previous models by a large margin.
- Classification/detection/segmentation 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
| name | pretrain | resolution | acc@1 | #params | FLOPs |
|---|---|---|---|---|---|
| InternImage-T | ImageNet-1K | 224x224 | 83.5 | 30M | 5G |
| InternImage-S | ImageNet-1K | 224x224 | 84.2 | 50M | 8G |
| InternImage-B | ImageNet-1K | 224x224 | 84.9 | 97M | 16G |
| InternImage-L | ImageNet-22K | 384x384 | 87.7 | 223M | 108G |
| InternImage-XL | ImageNet-22K | 384x384 | 88.0 | 335M | 163G |
COCO Object Detection
| backbone | method | lr schedule | box mAP | mask mAP | #params | FLOPs |
|---|---|---|---|---|---|---|
| InternImage-T | Mask R-CNN | 1x | 47.2 | 42.5 | 49M | 270G |
| InternImage-S | Mask R-CNN | 1x | 47.8 | 43.3 | 69M | 340G |
| InternImage-B | Mask R-CNN | 1x | 48.8 | 44.0 | 115M | 501G |
| InternImage-L | Cascade Mask R-CNN | 1x | 54.9 | 47.7 | 277M | 1399G |
| InternImage-XL | Cascade Mask R-CNN | 1x | 55.3 | 48.0 | 387M | 1782G |
ADE20K Semantic Segmentation
| backbone | resolution | single scale | multi scale | #params | FLOPs |
|---|---|---|---|---|---|
| InternImage-T | 512x512 | 47.9 | 48.1 | 59M | 944G |
| InternImage-S | 512x512 | 50.1 | 50.9 | 80M | 1017G |
| InternImage-B | 512x512 | 50.8 | 51.3 | 128M | 1185G |
| InternImage-L | 640x640 | 53.9 | 54.1 | 256M | 2526G |
| InternImage-XL | 640x640 | 55.0 | 55.3 | 368M | 3142G |
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}
}