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.

翻译：相较于近年来大规模视觉Transformer（ViTs）取得的显著进展，基于卷积神经网络（CNNs）的大规模模型仍处于早期阶段。本文提出一种名为InternImage的新型大规模CNN基础模型，该模型能够像ViTs一样从参数和训练数据的增加中获益。与近期聚焦于大密集核的CNN不同，InternImage以可变形卷积为核心算子，使模型不仅拥有检测与分割等下游任务所需的大有效感受野，还具备由输入和任务信息调节的自适应空间聚合能力。由此，所提出的InternImage降低了传统CNN的强归纳偏置，从而能够像ViTs一样从海量数据中通过大规模参数学习更强健的鲁棒模式。该模型在ImageNet、COCO和ADE20K等具有挑战性的基准测试中验证了其有效性。值得一提的是，InternImage-H在COCO test-dev上创下65.4 mAP的新纪录，在ADE20K上达到62.9 mIoU，超越了当前领先的CNN与ViT。代码将发布在 https://github.com/OpenGVLab/InternImage。