\begin{table}[t]
    \centering
    \setlength{\tabcolsep}{0.9mm}
    \footnotesize
    
\begin{tabular}{l|c|c|c|c|c}
	\renewcommand{\arraystretch}{0.1}
	Method & Type & Res & \#Params & \#FLOPs & Acc (\%)  \\
	\hline
	PVT-S~\cite{wang2021pyramid} & T &$224^2$  & 25M & 4G & 79.8 \\
	Swin-T~\cite{liu2021swin} & T &$224^2$ & 29M & 5G & 81.3\\
	CoAtNet-0~\cite{dai2021coatnet} & T &$224^2$ & 25M & 4G & 81.6 \\
	PVTv2-B2~\cite{wang2021pvtv2} & T & $224^2$  & 25M & 4G & 82.0 \\
	DeiT III-S~\cite{touvron2022deit3} & T &$224^2$  & 22M & 5G & 81.4\\
	SwinV2-T/8~\cite{liu2021swinv2} & T &$256^2$ & 28M & 6G & 81.8\\
	ConvNeXt-T~\cite{liu2022convnet} & C & $224^2$ & 29M & 5G & 82.1\\
	\rowcolor{gray!20}
	InternImage-T (ours) & C &$224^2$  & 30M & 5G & 83.5 \\
	\hline
	PVT-L~\cite{wang2021pyramid} & T &$224^2$  & 61M & 10G & 81.7 \\
	Swin-S~\cite{liu2021swin} & T &$224^2$  & 50M & 9G & 83.0\\
	CoAtNet-1~\cite{dai2021coatnet} & T &$224^2$ & 42M & 8G & 83.3 \\
	PVTv2-B4~\cite{wang2021pvtv2} & T & $224^2$  & 63M & 10G & 83.6 \\
	SwinV2-S/8~\cite{liu2021swinv2} & T &$256^2$ & 50M & 12G & 83.7\\
	ConvNeXt-S~\cite{liu2022convnet} & C &$224^2$  & 50M & 9G & 83.1\\
	\rowcolor{gray!20}
	InternImage-S (ours) & C &$224^2$  & 50M & 8G & 84.2 \\
	\hline
	Swin-B~\cite{liu2021swin} & T &$224^2$  & 88M & 15G & 83.5\\
	CoAtNet-2~\cite{dai2021coatnet} & T &$224^2$ & 75M & 16G & 84.1 \\
	PVTv2-B5~\cite{wang2021pvtv2} & T & $224^2$  & 82M & 12G & 83.8 \\
	DeiT III-B~\cite{touvron2022deit3} & T &$224^2$ & 87M & 18G & 83.8 \\
	SwinV2-B/8~\cite{liu2021swinv2} & T &$256^2$  & 88M & 20G & 84.2\\
	RepLKNet-31B~\cite{ding2022replknet} & C &$224^2$  & 79M & 15G & 83.5\\
	ConvNeXt-B~\cite{liu2022convnet} & C &$224^2$  & 88M & 15G & 83.8\\
	\rowcolor{gray!20}
	InternImage-B (ours) & C &$224^2$ & 97M & 16G & 84.9 \\
	\hline
	Swin-L$^\ddagger$~\cite{liu2021swin} & T &$384^2$ & 197M & 104G & 87.3\\
	CoAtNet-3$^\ddagger$~\cite{dai2021coatnet} & T &$384^2$ & 168M & 107G & 87.6 \\
	CoAtNet-4$^\ddagger$~\cite{dai2021coatnet} & T &$384^2$ & 275M & 190G & 87.9 \\
	DeiT III-L$^\ddagger$~\cite{touvron2022deit3} & T &$384^2$ & 304M & 191G & 87.7 \\
	SwinV2-L/24$^\ddagger$~\cite{liu2021swinv2} & T &$384^2$ & 197M & 115G & 87.6\\
	RepLKNet-31L$^\ddagger$~\cite{ding2022replknet} & C &$384^2$ & 172M & 96G & 86.6\\
	ConvNeXt-L$^\ddagger$~\cite{liu2022convnet} & C &$384^2$ & 198M & 101G & 87.5\\
	ConvNeXt-XL$^\ddagger$~\cite{liu2022convnet} & C &$384^2$ & 350M & 179G & 87.8\\
	\rowcolor{gray!10}
	InternImage-L$^\ddagger$ (ours) & C &$384^2$ & 223M & 108G & 87.7 \\
	\rowcolor{gray!20}
	InternImage-XL$^\ddagger$ (ours) & C &$384^2$ & 335M & 163G & 88.0 \\
	\hline
	ViT-G/14$^\#$~\cite{zhai2022scaling} & T & $518^2$  & 1.84B & 5160G & 90.5\\
	CoAtNet-6$^\#$~\cite{dai2021coatnet} & T & $512^2$ & 1.47B & 1521G & 90.5\\
	CoAtNet-7$^\#$~\cite{dai2021coatnet} & T & $512^2$ & 2.44B & 2586G & 90.9\\
	Florence-CoSwin-H$^\#$~\cite{yuan2021florence} & T & $-$ & 893M & $-$ & 90.0 \\
	SwinV2-G$^\#$~\cite{liu2021swinv2} & T & $640^2$ & 3.00B & $-$ & 90.2 \\
	RepLKNet-XL$^\#$~\cite{ding2022replknet} & C & $384^2$ & 335M & 129G & 87.8\\
	BiT-L-ResNet152x4$^\#$~\cite{kolesnikov2020big} & C & $480^2$ & 928M & $-$ & 87.5 \\
	\rowcolor{gray!10}
	InternImage-H$^\#$ (ours) & C & $224^2$ & 1.08B & 188G & 88.5  \\
	\rowcolor{gray!20}
	InternImage-H$^\#$ (ours) & C & $640^2$ & 1.08B & 1478G & 89.2  \\
\end{tabular}

    \caption{\textbf{Image classification performance on the ImageNet validation set}. ``Type'' refers to model type, where ``T'' and ``C'' denote transformer and CNN, respectively. ``Res'' means the input resolution.
    	``Acc'' is the top-1 accuracy.
    	``$^\ddagger$" indicates the model is pre-trained on ImageNet-22K~\cite{deng2009imagenet}. ``$^\#$" indicates pre-training on extra large-scale private dataset such as JFT-300M~\cite{xie2020self}, FLD-900M~\cite{yuan2021florence}, or the joint public dataset in this work.}
    \label{tab:cls_imagenet}
\end{table}
\author{
    Wenhai Wang$^{1*}$, 
    Jifeng Dai$^{2,1*}$,
    Zhe Chen$^{3,1*}$,
    Zhenhang Huang$^{1*}$,
    Zhiqi Li$^{3,1*}$,
    Xizhou Zhu$^{4*}$,\\
    Xiaowei Hu$^{1}$,
    Tong Lu$^{3}$,
    Lewei Lu$^{4}$,
    Hongsheng Li$^{5}$,
    Xiaogang Wang$^{3,5}$,
    Yu Qiao$^{1}$\textsuperscript{\Letter}\\
    $^1$ Shanghai AI Laboratory~~~
    $^2$Tsinghua University~~~\\
    $^3$Nanjing University~~~
    $^4$SenseTime Research~~~
    $^5$The Chinese University of Hong Kong\\
    {\small \url{https://github.com/OpenGVLab/InternImage}}
}

\textbf{Revisiting DCNv2.} A straightforward way to bridge the gap between convolution and MHSA is to introduce long-range dependencies and adaptive spatial aggregation into regular convolutions.
Let us start with DCNv2~\cite{zhu2019dcnv2}, which is a general variant of regular convolution.
Given an input $\mathbf{x}\!\in\!\mathbb{R}^{C\times H\times W}$ and current pixel $p_0$, DCNv2 can be formulated as:

where $K$ represents the total number of sampling points, and $k$ enumerates the sampling point. 
$\mathbf{w}_k\!\in\!\mathbb{R}^{C\times C}$ denotes the projection weights of the $k$-th sampling point,
and $\mathbf{m}_k\!\in\!\mathbb{R}$ represents the modulation scalar of the $k$-th sampling point, which is normalized by sigmoid function.
$p_k$ denotes the $k$-th location of the pre-defined grid sampling $\{(-1, -1), (-1, 0), ..., (0, +1), ..., (+1, +1)\}$ as in regular convolutions,
and $\Delta p_k$ is the offset corresponding to the $k$-th grid sampling location.
We see from the equation that (1) for long-range dependencies, the sampling offset $\Delta p_k$ is flexible and able to interact with short- or long-range features; and (2) for adaptive spatial aggregation, both the sampling offset $\Delta p_k$ and modulation scalar $\mathbf{m}_k$ are learnable and conditioned by input $\mathbf{x}$. 
So it can be found that \emph{DCNv2 shares similar favorable properties with MHSA}, which motivated us to develop large-scale CNN-based foundation models on the basis of this operator.

where $G$ denotes the total number of aggregation groups.
For the $g$-th group,
$\mathbf{w}_g\!\in\!\mathbb{R}^{C\times C'}$, $\mathbf{m}_{gk}\!\in\!\mathbb{R}$ denote the location-irrelevant projection weights of the group, where $C'\!=\!C/G$ represents the group dimension.
$\mathbf{m}_{gk}\!\in\!\mathbb{R}$ denotes the modulation scalar of the $k$-th sampling point in the $g$-th group, normalized by the softmax function along the dimension $K$.
$\mathbf{x}_g\!\in\!\mathbb{R}^{C'\times H \times W}$ represents the sliced input feature map.
$\Delta p_{gk}$ is the offset corresponding to the grid sampling location $p_k$ in the $g$-th group.

\textbf{Stem \& downsampling layers.}
To obtain hierarchical feature maps, we use 
convolutional stem and downsampling layers to resize the feature maps to different scales.
As shown in Fig. \ref{fig:arch}, the stem layer is placed before the first stage to reduce the input resolution by 4 times.
It consists of two convolutions, two LN layers, and one GELU layer,
where the kernel size of the two convolutions is 3, the stride is 2, the padding is 1,
and the output channel of the first convolution is half of the second one.
Similarly, the downsampling layer is made up of a 3$\times$3 convolution with a stride of 2 and a padding of 1, followed by one LN layer.
It sits between the two stages and is used to downsample the input feature map by 2 times.

where $\alpha\!\geq\!1$, $\beta\!\geq\!1$, and $\alpha\beta^{1.99}\!\approx\!2$. Here, 1.99 is specific for InternImage and calculated by doubling the model width and keeping the depth constant.
We experimentally find out that the best scaling setting is $\alpha\!=\!1.09$ and $\beta\!=\!1.36$, and then we base on it to construct InternImage\ variants with different parameter scales, namely InternImage-T/S/B/L/XL, whose complexity is similar to those of ConvNeXt~\cite{liu2022convnet}. 
To further test the capability, we built a larger InternImage-H with 1 billion parameters.
The detailed configurations are summarized in Table~\ref{tab:model_size}.

\subsection{Image Classification}
\textbf{Settings.}
We evaluate the classification performance of InternImage\ on ImageNet~\cite{deng2009imagenet}. 
For fair comparisons, following common practices~\cite{touvron2021training,wang2021pyramid,liu2021swin,liu2022convnet}, InternImage-T/S/B are trained on ImageNet-1K ($\sim$1.3 million) for 300 epochs, 
and InternImage-L/XL are first trained on ImageNet-22K ($\sim$14.2 million) for 90 epochs and then fine-tuned on ImageNet-1K for 30 epochs.
To further explore the capability of our model and match the large-scale private data used in previous methods~\cite{dai2021coatnet,liu2021swinv2,yuan2021florence}, we adopt M3I Pre-training~\cite{anonymous2022m2i}, an effective pre-training approach, to pre-train InternImage-H on a 427 million joint dataset of public Laion-400M~\cite{schuhmann2021laion}, YFCC-15M~\cite{thomee2016yfcc100m}, and CC12M~\cite{changpinyo2021conceptual} for 30 epochs,
and then we fine-tune the model on ImageNet-22K and -1K for 30 epochs, respectively.

\textbf{Results.} Table~\ref{tab:cls_imagenet} shows the classification results of models with different scales.
With comparable parameters and computational costs, our models are comparable or even superior to the state-of-the-art transformer-based and CNN-based models. 
For example, InternImage-T achieves 83.5\% top-1 accuracy, outperforming ConvNext-T~\cite{liu2022convnet} with a clear margin of 1.4 points.
InternImage-S/B keeps the leading position and surpasses the second-best method by at least 0.4 points.
When pre-trained on ImageNet-22K and the large-scale joint dataset, the top-1 accuracy of InternImage-XL and -H are boosted to 88.0\% and 89.2\%, respectively,
which is better than previous CNNs which are also trained with large-scale data, and closes the gap with state-of-the-art large transformer-based models to around 1 point.
This gap may be caused by the discrepancy between large-scale inaccessible private data and the aforementioned joint public data.
\emph{These results show that our InternImage\ not only has good performance on the common parameter scale and the public training data, but also can effectively extend to large-scale parameters and data.}
{"value": "25M", "type": "Hyper-parameter/Architecture", "model": "PVT-S", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "4G", "type": "Hyper-parameter/Architecture", "model": "PVT-S", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "79.8", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "PVT-S", "model settings": {"Type": "T"}} {"value": "29M", "type": "Hyper-parameter/Architecture", "model": "Swin-T", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "5G", "type": "Hyper-parameter/Architecture", "model": "Swin-T", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "81.3", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "Swin-T", "model settings": {"Type": "T"}} {"value": "25M", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-0", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "4G", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-0", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "81.6", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "CoAtNet-0", "model settings": {"Type": "T"}} {"value": "25M", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B2", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "4G", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B2", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "82.0", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "PVTv2-B2", "model settings": {"Type": "T"}} {"value": "22M", "type": "Hyper-parameter/Architecture", "model": "DeiT III-S", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "5G", "type": "Hyper-parameter/Architecture", "model": "DeiT III-S", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "81.4", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "DeiT III-S", "model settings": {"Type": "T"}} {"value": "28M", "type": "Hyper-parameter/Architecture", "model": "SwinV2-T/8", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "6G", "type": "Hyper-parameter/Architecture", "model": "SwinV2-T/8", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "81.8", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "SwinV2-T/8", "model settings": {"Type": "T"}} {"value": "29M", "type": "Hyper-parameter/Architecture", "model": "ConvNeXt-T", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "5G", "type": "Hyper-parameter/Architecture", "model": "ConvNeXt-T", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "82.1", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "ConvNeXt-T", "model settings": {"Type": "C"}} {"value": "30M", "type": "Hyper-parameter/Architecture", "model": "InternImage-T", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "5G", "type": "Hyper-parameter/Architecture", "model": "InternImage-T", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.5", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "InternImage-T", "model settings": {"Type": "C"}} {"value": "61M", "type": "Hyper-parameter/Architecture", "model": "PVT-L", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "10G", "type": "Hyper-parameter/Architecture", "model": "PVT-L", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "81.7", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "PVT-L", "model settings": {"Type": "T"}} {"value": "50M", "type": "Hyper-parameter/Architecture", "model": "Swin-S", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "9G", "type": "Hyper-parameter/Architecture", "model": "Swin-S", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.0", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "Swin-S", "model settings": {"Type": "T"}} {"value": "42M", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-1", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "8G", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-1", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.3", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "CoAtNet-1", "model settings": {"Type": "T"}} {"value": "63M", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B4", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "10G", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B4", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.6", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "PVTv2-B4", "model settings": {"Type": "T"}} {"value": "50M", "type": "Hyper-parameter/Architecture", "model": "SwinV2-S/8", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "12G", "type": "Hyper-parameter/Architecture", "model": "SwinV2-S/8", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.7", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "SwinV2-S/8", "model settings": {"Type": "T"}} {"value": "50M", "type": "Hyper-parameter/Architecture", "model": "ConvNeXt-S", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "9G", "type": "Hyper-parameter/Architecture", "model": "ConvNeXt-S", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.1", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "ConvNeXt-S", "model settings": {"Type": "C"}} {"value": "50M", "type": "Hyper-parameter/Architecture", "model": "InternImage-S", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "8G", "type": "Hyper-parameter/Architecture", "model": "InternImage-S", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "84.2", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "InternImage-S", "model settings": {"Type": "C"}} {"value": "88M", "type": "Hyper-parameter/Architecture", "model": "Swin-B", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "15G", "type": "Hyper-parameter/Architecture", "model": "Swin-B", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.5", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "Swin-B", "model settings": {"Type": "T"}} {"value": "75M", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-2", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "16G", "type": "Hyper-parameter/Architecture", "model": "CoAtNet-2", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "84.1", "type": "Result", "training data/set": "ImageNet-1K", "test data/set": "ImageNet validation", "task": "image classification", "metric": ["Acc", "top-1 accuracy"], "experimental settings": {"number of epochs": "300"}, "model": "CoAtNet-2", "model settings": {"Type": "T"}} {"value": "82M", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B5", "parameter/architecture name": ["# Params", "number of parameters"], "dataset": "ImageNet"} {"value": "12G", "type": "Hyper-parameter/Architecture", "model": "PVTv2-B5", "parameter/architecture name": ["# FLOPs", "number of FLOPs"], "dataset": "ImageNet"} {"value": "83.8", "type": 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