[论文翻译]用紧凑型Transformer (Compact Transformers) 逃离大数据范式

由 发布于 视觉模型语言处理大规模训练数据
 收藏

原文地址：https://arxiv.org/pdf/2104.05704v4


Escaping the Big Data Paradigm with Compact Transformers

用紧凑型Transformer (Compact Transformers) 逃离大数据范式

Abstract

摘要

With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to believe that because of this, transformers are not suitable for small sets of data. This trend leads to concerns such as: limited availability of data in certain scientific domains and the exclusion of those with limited resource from research in the field. In this paper, we aim to present an approach for small-scale learning by introducing Compact Transformers. We show for the first time that with the right size, convolutional token iz ation, transformers can avoid over fitting and outperform state-of-the-art CNNs on small datasets. Our models are flexible in terms of model size, and can have as little as 0.28M parameters while achieving competitive results. Our best model can reach $98%$ accuracy when training from scratch on CIFAR-10 with only 3.7M parameters, which is a significant improvement in data-efficiency over previous Transformer based models being over 10x smaller than other transformers and is $15%$ the size of ResNet50 while achieving similar performance. CCT also outperforms many modern CNN based approaches, and even some recent NAS-based approaches. Additionally, we obtain a new SOTA result on Flowers-102 with $99.76%$ top-1 accuracy, and improve upon the existing baseline on ImageNet $(82.71%$ accuracy with $29%$ as many parameters as ViT), as well as NLP tasks. Our simple and compact design for transformers makes them more feasible to study for those with limited computing resources and/or dealing with small datasets, while extending existing research efforts in data efficient transformers.

随着Transformer成为语言处理的标准模型，并在计算机视觉领域取得进展，其参数量与训练数据规模也相应增长。这使许多人认为Transformer不适用于小规模数据场景，由此引发诸多担忧：某些科学领域的数据可获得性受限，以及资源有限的研究者被排除在该领域研究之外。本文通过引入紧凑型Transformer (Compact Transformers) 提出小规模学习方法。我们首次证明：通过合理控制模型规模并采用卷积Token化 (convolutional tokenization) ，Transformer能够避免过拟合，并在小数据集上超越最先进的CNN模型。我们的模型具有参数量灵活性，最低仅需0.28M参数即可获得有竞争力的结果。在CIFAR-10数据集上，最优模型仅用3.7M参数从头训练即可达到98%准确率，其数据效率显著优于先前基于Transformer的模型——参数量比其他Transformer小10倍以上，且仅需ResNet50 15%的参数量即可达到相近性能。CCT还超越了许多基于CNN的现代方法，甚至部分最新的神经架构搜索 (NAS) 方法。此外，我们在Flowers-102数据集上取得99.76%的Top-1准确率（新SOTA结果），在ImageNet基准上提升现有基线（以ViT 29%的参数量达到82.71%准确率），并在NLP任务中表现优异。这种简洁紧凑的Transformer设计，为计算资源受限或处理小数据集的研究者提供了更可行的研究方案，同时拓展了数据高效Transformer的研究边界。


Figure 1: Overview of CVT (right), the basic compact transformer, and CCT (left), the convolutional variant of our compact transformer models. CCT can be quickly trained from scratch on small datasets, while achieving high accuracy (in under 30 minutes one can get $90%$ on an NVIDIA 2080Ti GPU or $80%$ on an AMD 5900X CPU on CIFAR-10 dataset).

图 1: CVT (右侧) 的基础紧凑型 Transformer 与 CCT (左侧) 的卷积变体模型概览。CCT 可在小型数据集上快速从头训练，同时实现高精度 (在 NVIDIA 2080Ti GPU 上 30 分钟内可达 CIFAR-10 数据集的 90%，AMD 5900X CPU 上可达 80%)。

1. Introduction

1. 引言

Convolutional neural networks (CNNs) [23] have been the standard for computer vision, since the success of AlexNet [22]. Krizhevsky et al. showed that convolutions are adept at vision based problems due to their invariance to spatial translations as well as having low relational inductive bias. He et al. [16] extended this work by introducing residual connections, allowing for significantly deeper models to perform efficiently. Convolutions leverage three im- portant concepts that lead to their efficiency: sparse interaction, weight sharing, and e qui variant representations [14]. Translational e qui variance and invariance are properties of the convolutions and pooling layers, respectively [14, 36]. They allow CNNs to leverage natural image statistics and subsequently allow models to have higher sampling efficiency [34, 34].

卷积神经网络 (CNNs) [23] 自 AlexNet [22] 取得成功后便成为计算机视觉领域的标准架构。Krizhevsky 等人证明了卷积运算凭借对空间平移的不变性及较低的关系归纳偏置，特别适合解决视觉相关问题。He 等人 [16] 通过引入残差连接扩展了这项工作，使得更深的模型能够高效运行。卷积运算利用三个关键概念实现高效性：稀疏交互 (sparse interaction)、权重共享 (weight sharing) 和等变表示 (equivariant representations) [14]。平移等变性和平移不变性分别是卷积层和池化层的特性 [14, 36]，这些特性使 CNN 能够利用自然图像的统计规律，从而让模型具备更高的采样效率 [34, 34]。

On the other end of the spectrum, Transformers have become increasingly popular and a major focus of modern machine learning research. Since the advent of Attention is All You Need [41], the research community saw a spike in transformer-based and attention-based research. While this work originated in natural language processing, these models have been applied to other fields, such as computer vision. Vision Transformer (ViT) [12] was the first major demonstration of a pure transformer backbone being applied to computer vision tasks. ViT highlights not only the power of such models, but also that large-scale training can trump inductive biases. The authors argued that “Transformers lack some of the inductive biases inherent to CNNs, such as translation e qui variance and locality, and therefore do not generalize well when trained on insufficient amounts of data.” Over the past few years, an explosion in model sizes and datasets has also become noticeable which has led to a “data hungry” paradigm, making training transformers from scratch seem intractable for many types of pressing problems, where there are typically several orders of magnitude less data. It also limits major contributions in the research to those with vast computational resources.

另一方面，Transformer 已成为现代机器学习研究的热点与主流方向。自《Attention is All You Need》[41] 发表以来，基于 Transformer 和注意力机制的研究呈现爆发式增长。尽管这类模型最初源于自然语言处理领域，现已被成功应用于计算机视觉等其他领域。Vision Transformer (ViT) [12] 首次证明了纯 Transformer 架构在计算机视觉任务中的有效性，不仅展现了该模型的强大能力，更揭示了大规模训练可以超越归纳偏置的局限性。作者指出："Transformer 缺乏 CNN 固有的归纳偏置（如平移等变性和局部性），因此在数据不足时泛化能力较差。"近年来，模型规模与数据集的爆炸式增长催生了"数据饥渴"范式，使得从头训练 Transformer 模型对于数据量通常低几个数量级的紧迫问题显得难以实现，同时也将重大研究突破的门槛抬高至需要庞大计算资源的机构。

As a result, CNNs are still the go-to models for smaller datasets because they are more efficient, both computationally and in terms of memory, when compared to transformers. Additionally, local inductive bias shows to be more important in smaller images. They require less time and data to train while also requiring a lower number of parameters to accurately fit data. However, they do not enjoy the long range interdependence that attention mechanisms in transformers provide. Reducing machine learning’s dependence on large sums of data is important, as many domains, such as science and medicine, would hardly have datasets the size of ImageNet [10]. This is because events are far more rare and it would be more difficult to properly assign labels, let alone create a set of data which has low bias and is appropriate for conventional neural networks. In medical research, for instance, it may be difficult to compile positive samples of images for a rare disease without other correlating factors, such as medical equipment being attached to patients who are actively being treated. Additionally, for a sufficiently rare disease there may only be a few thousand images for positive samples, which is typically not enough to train a network with good statistical prediction unless it can sufficiently be pre-trained on data with similar attributes. This inability to handle smaller datasets has impacted the scientific community where they are much more limited in the models and tools that they are able to explore. Frequently, problems in scientific domains have little in common with domains of pre-trained models and when domains are sufficiently distinct pre-training can have little to no effect on the performance within a new domain [54]. In addition, it has been shown that strong performance on

因此，CNN（卷积神经网络）仍是小数据集的首选模型，因为它们在计算和内存效率上优于Transformer。此外，局部归纳偏置在较小图像中显得更为重要。CNN需要更少的训练时间和数据，同时所需的参数量也更少，却能准确拟合数据。然而，它们无法像Transformer中的注意力机制那样捕捉长距离依赖关系。

降低机器学习对海量数据的依赖至关重要，因为在科学和医学等领域，几乎不可能获得ImageNet规模的数据集[10]。这些领域的事件极为罕见，正确标注标签已属不易，更遑论创建低偏差且适合传统神经网络的数据集。以医学研究为例，若要在不混杂其他相关因素（如患者治疗时连接的医疗设备）的情况下，收集某种罕见疾病的阳性样本图像可能极其困难。此外，对于极为罕见的疾病，阳性样本可能仅有数千张图像，这通常不足以训练出具有良好统计预测能力的网络，除非能在具有相似属性的数据上进行充分的预训练。

这种难以处理小数据集的局限性严重制约了科学界可探索的模型和工具。科学领域的问题往往与预训练模型的领域差异巨大，当领域差异足够显著时，预训练对新领域性能的影响微乎其微[54]。此外，研究表明......

ImageNet does not necessarily result in equally strong performance in other domains, such as medicine [20]. Furthermore, the requisite of large data results in a requisite of large computational resources and this prevents many researchers from being able to provide insight. This not only limits the ability to apply models in different domains, but also limits reproducibility. Verification of state of the art machine learning algorithms should not be limited to those with large infrastructures and computational resources.

ImageNet 在其他领域（如医学 [20]）未必能带来同样强劲的性能表现。此外，大数据需求必然导致高计算资源消耗，这使得许多研究者难以开展深入分析。这不仅限制了模型跨领域应用的能力，也影响了结果的可复现性。前沿机器学习算法的验证不应仅局限于拥有庞大基础设施和计算资源的机构。

The above concerns motivated our efforts to build more efficient models that can be effective in less data intensive domains and allow for training on datasets that are orders of magnitude smaller than those conventionally seen in computer vision and natural language processing (NLP) problems. Both Transformers and CNNs have highly desirable qualities for statistical inference and prediction, but each comes with their own costs. In this work, we try to bridge the gap between these two architectures and develop an architecture that can both attend to important features within images, while also being spatially invariant, where we have sparse interactions and weight sharing. This allows for a Transformer based model to be trained from scratch on small datasets like CIFAR-10 and CIFAR-100, providing competitive results with fewer parameters and low computational requirements.

上述考虑促使我们致力于构建更高效的模型，这些模型能在数据密集度较低的领域有效工作，并允许在比计算机视觉和自然语言处理 (NLP) 问题传统数据集小几个数量级的数据集上进行训练。Transformer 和 CNN 在统计推断和预测方面都具有极佳的特性，但各自也存在相应的成本。在这项工作中，我们尝试弥合这两种架构之间的差距，开发一种既能关注图像中的重要特征，又能保持空间不变性的架构，其中包含稀疏交互和权重共享。这使得基于 Transformer 的模型能在 CIFAR-10 和 CIFAR-100 等小型数据集上从头开始训练，以更少的参数和较低的计算需求提供具有竞争力的结果。

In this paper we introduce ViT-Lite, a smaller and more compact version of ViT, which can obtain over $90%$ accuracy on CIFAR-10. We expand on ViT-Lite by introducing a sequence pooling and forming the Compact Vision Transformer (CVT). We further iterate by adding convolutional blocks to the token iz ation step and thus creating the Compact Convolutional Transformer (CCT). Both of these simple additions add to significant increases in performance, leading to a top $1%$ accuracy of $98%$ on CIFAR-10. This makes our work the only transformer based model in the top 25 best performing models on CIFAR-10, without pretraining, and significantly smaller than the vast majority. Our model also outperforms most comparable CNN-based models within this domain, with the exception of certain Neural Architectural Search techniques [5]. Additionally, we show that our model can be lightweight, only needing 0.28 million parameters and still reach close to $90%$ top $1%$ accuracy on CIFAR-10. On ImageNet, CCT achieves $80.67%$ accuracy while still maintaining a small number of parameters and reduced computation. CCT outperforms ViT, while containing less than a third of the number of parameters with about a third of the computational complexity (MACs). Additionally, CCT outperform similarly sized and more recent models, such as DeiT [19]. This demonstrates the s cal ability of our model while maintaining compactness and computational efficiency.

本文介绍了ViT的精简版本ViT-Lite，该模型在CIFAR-10上可获得超过$90%$的准确率。我们通过引入序列池化机制扩展ViT-Lite，构建出紧凑视觉Transformer(CVT)。进一步在Token化阶段加入卷积模块，由此创建出紧凑卷积Transformer(CCT)。这两项简单改进显著提升了性能，使CIFAR-10的Top $1%$准确率达到$98%$。这使得我们的工作成为CIFAR-10性能前25名模型中唯一无需预训练的Transformer架构，且参数量远小于绝大多数模型。除某些神经架构搜索技术[5]外，我们的模型也优于该领域大多数基于CNN的对比模型。实验表明，我们的模型仅需28万参数即可在CIFAR-10上实现接近$90%$的Top $1%$准确率。在ImageNet上，CCT以较小参数量和计算量取得$80.67%$的准确率。相比ViT，CCT以不足三分之一的参数量(约三分之一计算复杂度/MACs)实现更优性能，同时优于DeiT[19]等同类新模型。这证明了我们的模型在保持紧凑性和计算效率的同时具备优秀的可扩展性。

The main contributions of this paper are:

本文的主要贡献包括：

In addition, we demonstrate that our CCT model is fast, obtaining $90%$ accuracy on CIFAR-10 using a single NVIDIA 2080Ti GPU and $80%$ when trained on a CPU (AMD 5900X), both in under 30 minutes. Additionally, since our model has a relatively small number of parameters, it can be trained on the majority of GPUs, even if researchers do not have access to top of the line hardware. Through these efforts, we aim to help enable and extend research around Transformers to cases with limited data and/or researchers with limited resources.

此外，我们证明CCT模型训练速度快：在单张NVIDIA 2080Ti GPU上对CIFAR-10数据集达到90%准确率，使用CPU (AMD 5900X) 训练时达到80%准确率，两者耗时均低于30分钟。由于模型参数量较小，即使研究人员不具备顶级硬件设备，也能在大多数GPU上完成训练。通过这些努力，我们希望能推动Transformer在数据有限或资源受限场景下的研究拓展。

2. Related Works

2. 相关工作

In NLP research, attention mechanisms [15, 2, 28] gained popularity for their ability to weigh different features within sequential data. Transformers [41] were introduced as a fully attention-based model, primarily for machine translation and NLP in general. Following this, attentionbased models, specifically transformers have been applied to a wide variety of tasks beyond machine translation [11, 25, 46], including: visual question answering [27, 38], action recognition [4, 13], and the like. Many researchers also leveraged a combination of attention and convolutions in neural networks for visual tasks [42, 18, 3, 51]. Ramachandran et al. [33] introduced one of the first vision models that rely primarily on attention. Do sov it ski y et al. [12] introduced the first stand-alone transformer based model for image classification (ViT). In the following subsections, we briefly revisit ViT and several other related works.

在自然语言处理(NLP)研究中，注意力机制 [15, 2, 28] 因其能够权衡序列数据中不同特征的能力而广受欢迎。Transformer [41] 被提出作为一种完全基于注意力的模型，主要用于机器翻译和一般的自然语言处理任务。随后，基于注意力的模型，特别是Transformer，被广泛应用于机器翻译之外的多种任务 [11, 25, 46]，包括：视觉问答 [27, 38]、动作识别 [4, 13] 等。许多研究者还结合了注意力机制和卷积神经网络来处理视觉任务 [42, 18, 3, 51]。Ramachandran等人 [33] 提出了首批主要依赖注意力的视觉模型之一。Dosovitskiy等人 [12] 提出了首个基于Transformer的独立图像分类模型(ViT)。在接下来的小节中，我们将简要回顾ViT及其他几项相关工作。

2.1. Vision Transformer

2.1. Vision Transformer

Do sov it ski y et al. [12] introduced ViT primarily to show that reliance on CNNs or their structure is unnecessary, as prior to it, most attention-based models for vision were used either with convolutions [42, 3, 51, 6], or kept some of their properties [33]. The motivation, beyond self-attention’s many desirable properties for a network, specifically its ability to make long range connections, was s cal ability. It was shown that ViT can successfully keep scaling, while CNNs start saturating in performance as the number of training samples grew. Through this, they concluded that large-scale training triumphs over the advantage of inductive bias that CNNs have, allowing their model to be competitive with CNN based architectures given sufficiently large amount of training data. ViT is composed of several parts: Image Token iz ation, Positional Embedding, Classification Token, the Transformer Encoder, and a Classification Head. These subjects are discussed in more detail below.

Do sov it ski y 等人 [12] 提出 ViT (Vision Transformer) 主要是为了证明无需依赖 CNN 或其结构。在此之前，大多数基于注意力的视觉模型要么与卷积结合使用 [42, 3, 51, 6]，要么保留了部分 CNN 特性 [33]。其动机除了自注意力机制具备网络所需的诸多优良特性（特别是长距离关联能力）外，还在于可扩展性。研究表明，随着训练样本数量增加，ViT 能持续保持性能提升，而 CNN 会出现性能饱和。由此得出结论：在大规模训练数据下，训练规模的优势会超越 CNN 的归纳偏置优势，使其模型能够与基于 CNN 的架构竞争。ViT 由以下几个部分组成：图像 Token 化 (Image Tokenization)、位置嵌入 (Positional Embedding)、分类 Token (Classification Token)、Transformer 编码器 (Transformer Encoder) 和分类头 (Classification Head)。下文将对这些部分进行详细讨论。

Image Token iz ation: A standard transformer takes as input a sequence of vectors, called tokens. For traditional NLP based transformers, word ordering provides a natural order to sequence the data, but this is not so obvious for images. To tokenize an image, ViT subdivides an image into non-overlapping square patches in raster-scan order. The sequence of patches, $\mathbf{x_{p}}\in\mathbb{R}^{H\times(P^{2}C)}$ with patch size $P$ , are flattened into 1D vectors and transformed into latent vectors of dimension $d$ . This is equivalent to a convolutional layer with $d$ filters, and $P\times P$ kernel size and stride. This simple patching and embedding method has a few limitations, in particular: loss of information along the boundary regions.

图像 Token 化：标准 Transformer 的输入是一组称为 Token 的向量序列。对于基于传统自然语言处理的 Transformer 而言，词语顺序为数据提供了自然的序列化依据，但图像则不然。ViT 采用光栅扫描顺序将图像划分为不重叠的方形图块来实现图像 Token 化。这些图块序列 $\mathbf{x_{p}}\in\mathbb{R}^{H\times(P^{2}C)}$（图块尺寸为 $P$）会被展平为一维向量，并转换为维度为 $d$ 的潜在向量。该过程等效于使用 $d$ 个滤波器、$P\times P$ 卷积核尺寸及步长的卷积层操作。这种简单的图块划分与嵌入方法存在若干局限性，尤其是边界区域的信息丢失问题。

Positional Embedding: Positional embedding adds spatial information into the sequence. Since the model does not actually know anything about the spatial relationship between tokens, adding extra information to reflect that can be useful. Typically, this is either a learned embedding or tokens are given weights from two sine waves with high frequencies, which is sufficient for the model to learn that there exists a positional relationship between these tokens.

位置编码 (Positional Embedding): 位置编码为序列添加空间信息。由于模型实际上并不了解 token 之间的空间关系，添加额外信息来反映这种关系会很有帮助。通常，这要么是通过学习得到的嵌入向量，要么是给 token 赋予来自两个高频正弦波的权重，这足以让模型学习到这些 token 之间存在位置关系。

Transformer Encoder: A transformer encoder consists of a series of stacked encoding layers. Each encoder layer is comprised of two sub-layers: Multi-Headed Self-Attention (MHSA) and a Multi-Layer Perceptron (MLP) head. Each sub-layer is preceded by a layer normalization (LN), and followed by a residual connection to the next sub-layer.

Transformer编码器：Transformer编码器由一系列堆叠的编码层组成。每个编码层包含两个子层：多头自注意力机制(MHSA)和多层感知机(MLP)头部。每个子层前都进行层归一化(LN)，并通过残差连接传递至下一子层。

Classification: Vision transformers typically add an extra learnable [class] token to the sequence of the embedded patches, representing the class parameter of an entire image and its state after transformer encoder can be used for classification. [class] token contains latent information, and through self-attention accumulates more information about the sequence, which is later used for classification. ViT [12] also explored averaging output tokens instead, but found no significant difference in performance. They did however find that the learning rates have to be adjusted between the two variants: [class] token vs. average pooling.

分类：视觉Transformer通常会在嵌入图像块的序列中添加一个额外的可学习[class] token，用于表示整个图像的类别参数，其经过Transformer编码器后的状态可用于分类。[class] token包含潜在信息，并通过自注意力机制积累更多关于序列的信息，最终用于分类。ViT [12]也尝试过使用平均输出token替代方案，但发现性能无明显差异。不过他们发现这两种变体（[class] token与平均池化）需要调整不同的学习率。

2.2. Data-Efficient Transformers

2.2. 数据高效的 Transformer

In an effort to reduce dependence on data, Touvron et al. [40] proposed Data-Efficient Image Transformers (DeiT). Using more advanced training techniques, and a novel knowledge transfer method, DeiT improves the classification performance of ViT on ImageNet-1k without large-scale pre-training on datasets such as JFT-300M [39] or ImageNet-21k [10]. By relying only on more augmentations [8] and training techniques [50, 49], it is shown that much smaller ViT variants that were unexplored by Dosovitskiy et al. can outperform the larger ones on ImageNet1k without pre-training. Furthermore, DeiT variants were pushed even further through their novel knowledge transfer technique, specifically when using a convolutional model as the teacher. This work pushes forward accessibility of transformers in medium-sized datasets, and we aim to follow by extending the study to even smaller sets of data and smaller models. However, we base our work on the notion that $i f$ a small dataset happens to be sufficiently novel, pre-trained models will not help train on that domain and the model will not be appropriate for that dataset. While knowledge transfer is a strong technique, it requires a pre-trained model for any given dataset, adding to training time and complexity, with an additional forward pass, and as pointed out by Touvron et al. is usually only significant when there’s a convolutional teacher available to transfer the inductive biases. As a result, it can be argued that if a network utilized just the bare minimum of convolutions, while keeping the pure transformer structure, it may need to rely less on large-scale training and transfer of inductive biases through knowledge transfer.

为减少对数据的依赖，Touvron等人[40]提出了数据高效图像Transformer（DeiT）。通过采用更先进的训练技术和新颖的知识迁移方法，DeiT在无需JFT-300M[39]或ImageNet-21k[10]等数据集大规模预训练的情况下，提升了ViT在ImageNet-1k上的分类性能。研究表明，仅依靠更强的数据增强[8]和训练技术[50,49]，Dosovitskiy等人未探索的更小ViT变体无需预训练即可在ImageNet1k上超越更大模型。此外，DeiT变体通过其新颖的知识迁移技术（特别是使用卷积模型作为教师网络时）实现了进一步突破。该研究推动了Transformer在中型数据集上的可及性，我们旨在将其研究延伸至更小规模的数据集和模型。但我们的工作基于以下假设：若小数据集本身具有足够新颖性，预训练模型将无法辅助该领域训练，且模型不适用于该数据集。虽然知识迁移是强大技术，但它要求针对每个数据集都需预训练模型，这会增加训练时间和复杂度（需额外前向传播），正如Touvron等人指出的，该方法通常仅在存在可迁移归纳偏置的卷积教师网络时效果显著。因此可以认为，若网络仅使用最基础的卷积操作并保持纯Transformer结构，或可减少对大规模训练和知识迁移中归纳偏置转移的依赖。

Yuan et al. [48] proposed Tokens-to-token ViT (T2TViT), which adopts a window- and attention-based tokenization strategy. Their tokenizer extracts patches of the input feature map, similar to a convolution, applies three sets of kernel weights, and produces three sets of feature maps, which are fed to self-attention as query and keyvalue pairs. This process is equivalent to convolutions producing the QKV projections in a self-attention module. Finally, this strategy is repeated twice, followed by a final patching and embedding. The entire process replaces patch and embedding in ViT. This strategy, along with their small-strided patch extraction, allows their network to model local structures, including along the boundaries between patches. This attention-based patch interaction leads to finer-grained tokens which allow T2T-ViT to outperform previous Transformer-based models on ImageNet. T2T-ViT differs from our work, in that it focuses on medium-sized datasets like ImageNet, which are not only far too large for many research problems in science and medicine but also resource demanding. T2T tokenizer also has more parameters and complexity compared to a convolutional one.

Yuan等人[48]提出了Tokens-to-token ViT (T2TViT)，采用基于窗口和注意力的token化策略。该token提取器类似卷积操作提取输入特征图的局部块，应用三组卷积核权重生成三组特征图，作为查询和键值对输入自注意力模块。这一过程等价于在自注意力模块中用卷积生成QKV投影。最终重复两次该策略后执行最后一次分块嵌入，整体流程替代了ViT中的分块嵌入步骤。结合小步长分块提取策略，该网络能够建模局部结构（包括分块边界区域）。这种基于注意力的分块交互机制产生了更细粒度的token，使得T2T-ViT在ImageNet上超越了先前基于Transformer的模型。T2T-ViT与我们的工作差异在于：其聚焦ImageNet等中型数据集，这类数据集不仅远超科学和医学领域多数研究问题的规模需求，且计算资源消耗大；相比卷积式token化方案，T2T方案还具有更高的参数量与复杂度。

2.3. Convolution-inspired Transformers

2.3. 卷积启发的Transformer

Many works have been motivated to improve vision transformers and eliminate the need for large-scale pretraining. ConViT [9] introduces a gated positional selfattention (GPSA) that allows for a “soft” convolutional inductive bias within their model. GPSA allows their network to have more flexibility with respect to positional information. Since GPSA is able to be initialized as a convolutional layer, this allows their network to sometimes have the properties of convolutions or alternatively having the properties of attention. Its gating parameter can be adjusted by the network, allowing it to become more expressive and adapt to the needs of the dataset. Convolutionenhanced image Transformers (Ceit) [47] utilize convolutions throughout their model. They propose a convolutionbased Image-to-Token module for token iz ation. They also re-design the encoder with layers of multi-headed selfattention and their novel Locally Enhanced Feed forward Layer, which processes the spatial information form the extracted token. This allows creates a network that is competitive with other works such as DeiT on ImageNet. Convolutional vision Transformer (CvT) [45] introduces convolutional transformer encoder layers, which use convolutions instead of linear projections for the QKV in selfattention. They also introduce convolutions into their tokenization step, and report competitive results compared to other vision transformers on ImageNet-1k. All of these works report results when trained from scratch on ImageNet (or larger) datasets.

许多研究致力于改进视觉Transformer (Vision Transformer) 并消除对大规模预训练的依赖。ConViT [9] 提出了一种门控位置自注意力 (GPSA) 机制，在模型中实现了"软"卷积归纳偏置。GPSA 使网络对位置信息具有更高的灵活性。由于 GPSA 可初始化为卷积层，该网络既能保留卷积特性，也能展现注意力机制的特性。其门控参数可由网络动态调整，从而增强表达能力并适应数据集需求。卷积增强图像Transformer (Ceit) [47] 在模型中全程采用卷积操作，提出基于卷积的 Image-to-Token 模块进行 Token 化，并通过多头自注意力层与新颖的局部增强前馈层重构编码器，后者专门处理提取 Token 的空间信息。该网络在 ImageNet 上取得了与 DeiT 等模型相当的性能。卷积视觉Transformer (CvT) [45] 引入卷积Transformer编码层，用卷积替代自注意力中 QKV 的线性投影，并在 Token 化阶段融入卷积操作，在 ImageNet-1k 上报告了优于其他视觉Transformer的结果。这些工作均在 ImageNet（或更大规模）数据集上从头训练并验证效果。

2.4. Comparison

2.4. 对比

Our work differs from the aforementioned in several ways, in that it focuses on answering the following question: Can vision transformers be trained from scratch on small datasets? Focusing on a small datasets, we seek to create a model that can be trained, from scratch, on datasets that are orders of magnitude smaller than ImageNet. Having a model that is compact, small in size, and efficient allows greater accessibility, as training on ImageNet is still a difficult and data intensive task for many researchers. Thus our focus is on an accessible model, with few parameters, that can quickly and efficiently be trained on smaller platforms while still maintaining SOTA results.

我们的工作与上述研究在几个方面存在差异，重点在于回答以下问题：视觉Transformer (Vision Transformer) 能否在小规模数据集上从头训练？聚焦小规模数据，我们致力于构建一个可在比ImageNet小几个数量级的数据集上从头训练的模型。紧凑、轻量且高效的模型能显著提升可及性，因为对许多研究者而言，ImageNet训练仍是数据密集的高难度任务。因此我们专注于开发参数少、可访问的模型，使其能在小型平台上快速高效训练，同时保持SOTA (State-of-the-Art) 性能。

3. Method

3. 方法

In order to provide empirical evidence that vision transformers are trainable from scratch when dealing with small sets of data, we propose three different models: ViTLite, Compact Vision Transformers (CVT), and Compact Convolutional Transformers (CCT). ViT-Lite is nearly identical to the original ViT in terms of architecture, but with a more suitable size and patch size for small-scale learning. CVT builds on this by using our Sequence Pooling method (SeqPool), that pools the entire sequence of tokens produced by the transformer encoder. SeqPool replaces the conventional [class] token. CCT builds on CVT and utilizes a convolutional tokenizer, generating richer tokens and preserving local information. The convolutional tokenizer is better at encoding relationships between patches compared to the original ViT [12]. A detailed modular-level comparison of these models can be viewed in Figure 2.

为了提供视觉Transformer在小规模数据集上可从头训练的实证依据，我们提出了三种模型：ViTLite、紧凑视觉Transformer（CVT）和紧凑卷积Transformer（CCT）。ViT-Lite在架构上与原始ViT几乎相同，但针对小规模学习调整了更合适的模型尺寸和图像块尺寸。CVT在此基础上采用我们提出的序列池化方法（SeqPool），该方法对Transformer编码器输出的全部Token序列进行池化操作，替代了传统的[class] Token。CCT在CVT框架中引入卷积Tokenizer，通过生成更具表现力的Token来保留局部信息。与原始ViT [12] 相比，这种卷积Tokenizer能更有效地编码图像块间关系。各模型的模块级详细对比见图2:


Figure 2: Comparing ViT (top) to CVT (middle) and CCT (bottom). CVT can be thought of as an ablated version of CCT, only utilizing sequence pooling and not a convolutional tokenizer. CVT may be preferable with more limited compute, as the patch-based token iz ation is faster.

图 2: ViT (上) 与 CVT (中) 和 CCT (下) 的对比。CVT 可视为 CCT 的简化版本，仅使用序列池化而不采用卷积分词器。在计算资源受限时，基于图像块的分词方式速度更快，因此 CVT 可能是更优选择。

The components of our compact transformers are further discussed in the following subsections: Transformer-based Backbone, Small and Compact Models, SeqPool, and Convolutional Tokenizer.

我们紧凑型 Transformer 的组件将在以下小节中进一步讨论：基于 Transformer 的主干网络、小型紧凑模型、SeqPool 和卷积 Tokenizer。

3.1. Transformer-based Backbone

3.1. 基于Transformer的主干网络

In terms of model design, we follow the original Vision Transformer [12], and original Transformer [41]. As mentioned in Section 2.1, the encoder consists of transformer blocks, each including an MHSA layer and an MLP block. The encoder also applies Layer Normalization, $G E L U$ activation, and dropout. Positional embeddings can be learnable or sinusoidal, both of which are effective.

在模型设计方面，我们遵循原始Vision Transformer [12]和原始Transformer [41]。如第2.1节所述，编码器由transformer块组成，每个块包含一个MHSA层和一个MLP块。编码器还应用了层归一化、$GELU$激活和dropout。位置嵌入可以是可学习的或正弦式的，这两种方式都有效。

3.2. Small and Compact Models

3.2. 小型紧凑模型

We propose smaller and more compact vision transformers. The smallest ViT variant, ViT-Base, includes a 12 layer transformer encoder with 12 attention heads, 64 dimensions per head, and 2048-dimensional hidden layers in the MLP blocks. This, along with the classifier and 16x16 patch and embedder results in over 85M parameters. We propose variants with as few as 2 layers, 2 heads, and 128-dimensional hidden layers. In Appendix A, we summarized the details of the variants we propose, the smallest of which can have as little as 0.22M parameters, while the largest (for small-scale learning) only have 3.8M parameters. We also adjust the tokenizer (patch size) according to the dataset we’re training on, based on its image resolution. These variants, which are mostly similar in architecture to ViT, but different in size, are referred to as ViT-Lite. In our notation, we use the number of layers to specify size, as well as token iz ation details: for instance, ViT-Lite-12/16 has 12 transformer encoder layers, and a $\mathbf{16}\mathbf{\times16}$ patch size.

我们提出更小更紧凑的视觉Transformer。最小的ViT变体ViT-Base包含12层Transformer编码器，每层12个注意力头，每个头64维，MLP模块中2048维的隐藏层。加上分类器、16x16图像块划分及嵌入层，参数量超过8500万。我们提出的变体最少仅需2层、2个注意力头和128维隐藏层。附录A总结了这些变体的细节，最小变体参数量可低至22万，最大变体(针对小规模学习)仅380万参数。我们还根据训练数据集的图像分辨率调整分词器(图像块大小)。这些架构与ViT基本相似但尺寸不同的变体称为ViT-Lite。在命名规则中，我们用层数表示模型规模并注明分词细节：例如ViT-Lite-12/16表示具有12层Transformer编码器，采用$\mathbf{16}\mathbf{\times16}$图像块划分。

3.3. SeqPool

3.3. SeqPool

In order to map the sequential outputs to a singular class index, ViT [12] and most other common transformer-based class if i ers follow BERT [11], in forwarding a learnable class or query token through the network and later feeding it to the classifier. Other common practices include global average pooling (averaging over tokens), which have been shown to be preferable in some scenarios. We introduce SeqPool, an attention-based method which pools over the output sequence of tokens. Our motivation is that the output sequence contains relevant information across different parts of the input image, therefore preserving this information can improve performance, and at no additional parameters compared to the learnable token. Additionally, this change slightly decreases computation, due one less token being forwarded. This operation consists of mapping the output sequence using the transformation T : Rb×n×d Rb×d. Given:

为了将序列输出映射到单一类别索引，ViT [12] 和大多数其他基于Transformer的分类器遵循BERT [11]的做法，通过网络传递一个可学习的类别或查询token，随后将其输入分类器。其他常见方法包括全局平均池化（对token取平均），在某些场景下已被证明更优。我们提出了SeqPool，这是一种基于注意力的方法，对输出token序列进行池化。我们的动机是输出序列包含了输入图像不同部分的相关信息，因此保留这些信息可以提升性能，且相比可学习token不会增加额外参数。此外，由于减少了一个token的前向传递，这一改动还略微降低了计算量。该操作通过变换T : ℝᵇ×ⁿ×ᵈ → ℝᵇ×ᵈ实现序列输出的映射。给定：

$$
\mathbf{x}{L}=\mathrm{f}(\mathbf{x}_{0})\in\mathbb{R}^{b\times n\times d}
$$

$$
\mathbf{x}{L}=\mathrm{f}(\mathbf{x}_{0})\in\mathbb{R}^{b\times n\times d}
$$

where $\mathbf{x}{L}$ is the output of an $L$ layer transformer encoder $f,b$ is batch size, $n$ is sequence length, and $d$ is the total embedding dimension. $\mathbf{x}{L}$ is fed to a linear layer $\mathrm{g}(\mathbf{x}_{L})\in$ $\mathbb{R}^{d\times1}$ , and softmax activation is applied to the output:

其中 $\mathbf{x}{L}$ 是 $L$ 层 Transformer 编码器 $f$ 的输出，$b$ 为批大小，$n$ 为序列长度，$d$ 为总嵌入维度。$\mathbf{x}{L}$ 被输入线性层 $\mathrm{g}(\mathbf{x}_{L})\in$ $\mathbb{R}^{d\times1}$，并对输出应用 softmax 激活函数：

$$
\mathbf{x}{L}^{\prime}=\mathrm{softmax}\left(\mathbf{g}(\mathbf{x}_{L})^{T}\right)\in\mathbb{R}^{b\times1\times n}
$$

$$
\mathbf{x}{L}^{\prime}=\mathrm{softmax}\left(\mathbf{g}(\mathbf{x}_{L})^{T}\right)\in\mathbb{R}^{b\times1\times n}
$$

This generates an importance weighting for each input token, which is applied as follows:

这为每个输入Token生成一个重要性权重，其应用方式如下：

$$
\mathbf{z}=\mathbf{x}{L}^{\prime}\mathbf{x}{L}=\mathrm{softmax}\left(\mathbf{g}(\mathbf{x}{L})^{T}\right)\times\mathbf{x}_{L}\in\mathbb{R}^{b\times1\times d}
$$

$$
\mathbf{z}=\mathbf{x}{L}^{\prime}\mathbf{x}{L}=\mathrm{softmax}\left(\mathbf{g}(\mathbf{x}{L})^{T}\right)\times\mathbf{x}_{L}\in\mathbb{R}^{b\times1\times d}
$$

By flattening, the output $\boldsymbol{z}\in\mathbb{R}^{b\times d}$ is produced. This output can then be sent through a classifier.

通过展平操作，生成输出 $\boldsymbol{z}\in\mathbb{R}^{b\times d}$。该输出随后可输入分类器。

SeqPool allows our network to weigh the sequential embeddings of the latent space produced by the transformer encoder and correlate data across the input data. This can be thought of this as attending to the sequential data, where we are assigning importance weights across the sequence of data, only after they have been processed by the encoder. We tested several variations of this pooling method, including learnable and static methods, and found that the learnable pooling performs the best. Static methods, such as global average pooling have already been explored by ViT as well, as pointed out in section 2.1. We believe that the learnable weighting is more efficient because each embedded patch does not contain the same amount of entropy. This allows the model to apply weights to tokens with respect to the relevance of their information. Additionally, sequence pooling allows our model to better utilize information across spatially sparse data. We will further study the effects of this pooling in the ablation study (Sec 4.4). By replacing the conventional class token in ViT-Lite with SeqPool, Compact Vision Transformer is created. We use the same notations for this model: for instance, CVT-7/4 has 7 transformer encoder layers, and a $4{\times}4$ patch size.

SeqPool 使我们的网络能够权衡由 Transformer 编码器生成的潜在空间中的序列嵌入，并在输入数据之间建立关联。这可以理解为对序列数据进行注意力加权，即在数据经过编码器处理后，为整个数据序列分配重要性权重。我们测试了该池化方法的多种变体（包括可学习和静态方法），发现可学习池化表现最佳。如第 2.1 节所述，ViT 也已探索过全局平均池化等静态方法。我们认为可学习加权更高效，因为每个嵌入块包含的熵值并不相同。这使得模型能根据 Token 信息的相关性分配权重。此外，序列池化使模型能更好地利用空间稀疏数据中的信息。我们将在消融研究中进一步分析该池化的影响（第 4.4 节）。通过用 SeqPool 替换 ViT-Lite 中的传统类别 Token，我们构建了紧凑视觉 Transformer (Compact Vision Transformer)。沿用相同命名规则：例如 CVT-7/4 表示具有 7 个 Transformer 编码器层和 $4{\times}4$ 分块尺寸。

3.4. Convolutional Tokenizer

3.4. 卷积分词器 (Convolutional Tokenizer)

In order to introduce an inductive bias into the model, we replace patch and embedding in ViT-Lite and CVT, with a simple convolutional block. This block follows conventional design, which consists of a single convolution, ReLU activation, and a max pool. Given an image or feature map x RH×W ×C:

为了在模型中引入归纳偏置，我们将ViT-Lite和CVT中的图像块(patch)与嵌入(embedding)替换为一个简单的卷积块。该模块采用常规设计，包含单层卷积、ReLU激活函数和最大池化操作。给定输入图像或特征图x∈R^(H×W×C)：

$$
\mathbf{x}_{0}=\mathrm{MaxPool}(\mathrm{ReLU}(\mathrm{Conv2d}(\mathbf{x})))
$$

$$
\mathbf{x}_{0}=\mathrm{MaxPool}(\mathrm{ReLU}(\mathrm{Conv2d}(\mathbf{x})))
$$

where the Conv2d operation has $d$ filters, same number as the embedding dimension of the transformer backbone. Additionally, the convolution and max pool operations can be overlapping, which could increase performance by injecting inductive biases. This allows our model to maintain locally spatial information. Additionally, by using this convolutional block, the models enjoy an added flexibility over models like ViT, by no longer being tied to the input resolution strictly divisible by the pre-set patch size. We seek to use convolutions to embed the image into a latent represent ation, because we believe that it will be more efficient and produce richer tokens for the transformer. These blocks can be adjusted in terms of down sampling ratio (kernel size, stride and padding), and are repeatable for even further down sampling. Since self-attention has a quadratic time and space complexity with respect to the number of tokens, and number of tokens is equal to the resolution of the input feature map, more down sampling results in fewer tokens which noticeably decreases computation (at the expense of performance). We found that on top of the added performance gains, this choice in token iz ation also gives more flexibility toward removing the positional embedding in the model, as it manages to maintain a very good performance. This is further discussed in Appendix C.1.

其中 Conv2d 操作具有 $d$ 个滤波器，数量与 Transformer 主干的嵌入维度相同。此外，卷积和最大池化操作可以重叠，这能通过注入归纳偏置提升性能。该设计使模型能保留局部空间信息。同时，得益于该卷积块，模型相比 ViT 等架构获得了额外灵活性，不再严格受限于输入分辨率必须被预设分块大小整除的条件。我们采用卷积将图像嵌入潜在表征空间，因为该方式效率更高，能为 Transformer 生成更丰富的 Token。这些模块的下采样比例（核尺寸、步长和填充）可调，并可重复堆叠以实现更深层下采样。由于自注意力机制的时间空间复杂度与 Token 数量呈平方关系，而 Token 数量等于输入特征图分辨率，更多下采样会显著减少 Token 数量从而降低计算量（以性能为代价）。实验表明，这种 Token 化方案不仅能带来性能提升，还为去除模型中的位置嵌入提供了更大灵活性（因其仍能保持优异性能），详见附录 C.1。

This convolutional tokenizer, along with SeqPool and the transformer encoder create Compact Convolutional Transformers. We use a similar notation for CCT variants, with the exception of also denoting the number of convolutional layers: for instance, $\mathrm{CCT}\mathrm{-}7/3\mathrm{x}2$ has 7 transformer encoder layers, and a 2-layer convolutional tokenizer with $\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}\hphantom{000}$ kernel size.

该卷积分词器与SeqPool以及Transformer编码器共同构成了紧凑型卷积Transformer (Compact Convolutional Transformers)。我们采用类似的命名规则来区分CCT变体，但需额外标注卷积层数：例如$\mathrm{CCT}\mathrm{-}7/3\mathrm{x}2$表示包含7层Transformer编码器，以及采用$3\times3$卷积核的2层卷积分词器。

4. Experiments

4. 实验


Figure 3: CIFAR-10 accuracy vs. model size (sizes $<12\mathbf{M})$ . $\mathrm{CCT^{\star}}$ was trained longer.

图 3: CIFAR-10 准确率与模型规模 (规模 $<12\mathbf{M})$ 的关系。$\mathrm{CCT^{\star}}$ 训练时间更长。

Table 1: Top-1 validation accuracy comparisons. $\star$ variants were trained longer (see Table 2 )

表 1: Top-1验证准确率对比。带$\star$的变体训练时间更长(见表2)

模型 C-10 C-100 Fashion MNIST 参数量 MACs
卷积网络(专为ImageNet设计)
ResNet18 90.27% 66.46% 94.78% 99.80% 11.18 M 0.04 G
ResNet34 90.51% 66.84% 94.78% 99.77% 21.29 M 0.08 G
MobileNetV2/0.5MobileNetV2/2.0 84.78%91.02% 56.32%67.44% 93.93%95.26% 99.70%99.75% 0.70 M8.72 M < 0.01 G0.02 G
卷积网络(专为CIFAR设计)
ResNet56[16]ResNet110[16] 94.63%95.08% 74.81%76.63% 95.25%95.32% 99.27%99.28% 0.85 M1.73 M 0.13 G0.26 G
ResNet1k-v2*[17]Proxyless-G[5] 95.38%97.92% - - - 10.33 M5.7 M 1.55 G
Vision Transformers
ViT-12/16 83.04% 57.97% 93.61% 99.63% 85.63 M 0.43 G
ViT-Lite-7/16 78.45% 52.87% 93.24% 99.68% 3.89 M 0.02 G
ViT-Lite-7/8 89.10% 67.27% 94.49% 99.69% 3.74 M 0.06 G
ViT-Lite-7/4 93.57% 73.94% 95.16% 99.77% 3.72 M 0.26 G
CompactVisionTransformers
CVT-7/8 89.79% 70.11% 94.50% 99.70% 3.74 M 0.06 G
CVT-7/4 94.01% 76.49% 95.32% 99.76% 3.72 M 0.25 G
CompactConvolutionalTransformers
CCT-2/3x2CCT-7/3x2 89.75%95.04% 66.93%77.72% 94.08%95.16% 99.70%99.76% 0.28 M3.85 M 0.04 G0.29 G
CCT-7/3x1 96.53% 80.92% - - 3.76 M 1.19 G
CCT-7/3x1* 98.00% 82.72% 95.56% 99.82% 3.76 M 1.19 G

Table 2: CCT $.7/3\times1$ top-1 accuracy on CIFAR-10/100 when trained longer

表 2: CCT $.7/3\times1$ 在 CIFAR-10/100 上长时间训练后的 top-1 准确率

#Epochs Pos. Emb. CIFAR-10 CIFAR-100
300 Learnable 96.53% 80.92%
1500 Sinusoidal 97.48% 82.72%
5000 Sinusoidal 98.00% 82.87%

MNIST and Fashion-MNIST only contain a single channel, greatly reducing the information density. Flowers-102 has a relatively small number of samples, while having relatively higher resolution images and 102 classes. We divided these datasets into three categories: small-scale small resolution datasets (CIFAR-10/100, MNIST, and FashionMNIST), small-scale larger resolution (Flowers-102), and medium-scale (ImageNet-1k) datasets. We also include a study on NLP classification, presented in appendix G.

MNIST和Fashion-MNIST仅包含单通道，显著降低了信息密度。Flowers-102样本数量较少，但图像分辨率较高且包含102个类别。我们将这些数据集分为三类：小规模低分辨率数据集（CIFAR-10/100、MNIST和FashionMNIST）、小规模高分辨率（Flowers-102）以及中规模（ImageNet-1k）数据集。我们还进行了NLP分类研究，详见附录G。

4.1. Datasets

4.1. 数据集

We conducted image classification experiments using our method on the following datasets: CIFAR-10, CIFAR100 (MIT License) [21], MNIST, Fashion-MNIST, Oxford Flowers-102 [30], and ImageNet-1k [10]. The first four datasets not only have a small number of training samples, but they are also small in resolution. Additionally,

我们使用以下数据集进行了图像分类实验：CIFAR-10、CIFAR-100 (MIT License) [21]、MNIST、Fashion-MNIST、Oxford Flowers-102 [30] 和 ImageNet-1k [10]。前四个数据集不仅训练样本数量少，而且分辨率也较低。此外，

4.2. Hyper parameters

4.2. 超参数

We used the timm package [43] to train the models (see Appendix E for details), except for cited works which are reported directly. For all experiments, we conducted a hyper parameter sweep for every different method and report the best results we were able to achieve. We will release all checkpoints corresponding to the reported numbers, and detailed training settings in the form of YAML files, with our code. We also provide a report on hyper param ter settings in Appendix E. Unless stated otherwise, all tests were run for 300 epochs, and the learning rate is reduced per epoch based on cosine annealing [26]. All transformer based models (ViT-Lite, CVT, and CCT) were trained using the AdamW optimizer.

我们使用 timm 包 [43] 训练模型（详见附录 E），直接引用的工作除外。所有实验均针对每种不同方法进行超参数扫描，并报告我们取得的最佳结果。我们将发布与报告数值对应的所有检查点，以及以 YAML 文件形式记录的详细训练设置（随代码一同发布）。附录 E 还提供了超参数设置报告。除非另有说明，所有测试均运行 300 个周期，学习率根据余弦退火 [26] 逐周期降低。所有基于 Transformer 的模型（ViT-Lite、CVT 和 CCT）均使用 AdamW 优化器进行训练。

Table 3: ImageNet Top-1 validation accuracy comparison (no extra data or pre training). This shows that larger variants of CCT could also be applicable to medium-sized datasets

表 3: ImageNet Top-1 验证准确率对比 (无额外数据或预训练)。这表明更大规模的 CCT 变体也可能适用于中等规模数据集

模型 Top-1 参数量 MACs 训练周期
ResNet50 [16] 77.15% 25.55M 4.15 G 120
ResNet50 (2021) [44] 79.80% 25.55M 4.15 G 300
ViT-S [19] 79.85% 22.05M 4.61 G 300
CCT-14/7×2 80.67% 22.36 M 5.53 G 300
DeiT-S [19] 81.16% 22.44M 4.63 G 300
CCT-14/7x2Distilled 81.34% 22.36M 5.53G 300

Table 4: Flowers-102 Top-1 validation accuracy comparison. CCT outperforms other competitive models, having significantly fewer parameters and GMACs. This demonstrates the compactness on small datasets even with large images

表 4: Flowers-102 Top-1验证准确率对比。CCT以显著更少的参数量和GMACs超越了其他竞争模型，这证明了即使在大图像的小型数据集上也具有紧凑性

模型 分辨率 预训练 Top-1 #Params MACs
CCT-14/7×2 224 97.19% 22.17 M 18.63 G
DeiT-B 384 ImageNet-1k 98.80% 86.25M 55.68G
ViT-L/16 384 JFT-300M 99.74% 304.71M 191.30G
ViT-H/14 384 JFT-300M 99.68% 661.00M 504.00G
CCT-14/7×2 384 ImageNet-1k 99.76% 22.17M 18.63 G

4.3. Performance Comparison

4.3. 性能对比

Small-scale small resolution training: In order to demonstrate that vision transformers can be as effective as convolutional neural networks, even in settings with small sets of data, we compare our compact transformers to ResNets [16], which are still very useful CNNs for small to medium amounts of data, as well as to Mobile Ne tV 2 [35], which are very compact and small-sized CNNs. We also compare with results from [17] where He et al. designed very deep (up to 1001 layers) CNNs specifically for CIFAR. The re