  Compared to the great progress of large-scale vision transformers (ViTs) in
recent years, large-scale models based on convolutional neural networks (CNNs)
are still in an early state. This work presents a new large-scale CNN-based
foundation model, termed InternImage, which can obtain the gain from increasing
parameters and training data like ViTs. Different from the recent CNNs that
focus on large dense kernels, InternImage takes deformable convolution as the
core operator, so that our model not only has the large effective receptive
field required for downstream tasks such as detection and segmentation, but
also has the adaptive spatial aggregation conditioned by input and task
information. As a result, the proposed InternImage reduces the strict inductive
bias of traditional CNNs and makes it possible to learn stronger and more
robust patterns with large-scale parameters from massive data like ViTs. The
effectiveness of our model is proven on challenging benchmarks including
ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved
a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming
current leading CNNs and ViTs. The code will be released at
https://github.com/OpenGVLab/InternImage.
