Designing a high-efficiency and high-quality expressive network architecture has always been the most important research topic in the field of deep learning. Most of today's network design strategies focus on how to integrate features extracted from different layers, and how to design computing units to effectively extract these features, thereby enhancing the expressiveness of the network. This paper proposes a new network design strategy, i.e., to design the network architecture based on gradient path analysis. On the whole, most of today's mainstream network design strategies are based on feed forward path, that is, the network architecture is designed based on the data path. In this paper, we hope to enhance the expressive ability of the trained model by improving the network learning ability. Due to the mechanism driving the network parameter learning is the backward propagation algorithm, we design network design strategies based on back propagation path. We propose the gradient path design strategies for the layer-level, the stage-level, and the network-level, and the design strategies are proved to be superior and feasible from theoretical analysis and experiments.

Yolov7在5 fps到160 fps的速度和准确性上都超过了所有已知对象探测器，并且在GPU V100上具有30 fps或更高的所有已知实时对象探测器中，精度最高的56.8％AP。YOLOV7-E6对象检测器（56 fps v100，55.9％AP）优于两个基于变压器的检测器SWIN-L-CASCADE MAKS R-CNN（9.2 fps A100，53.9％AP）的速度和2％的准确性和2％基于卷积的检测器Convnext-XL级联膜面罩R-CNN（8.6 fps a100，55.2％AP）的速度为551％，精度为0.7％AP，Yolov7优于：Yolor，Yolox，Yolox，Scaled-Yolov4，Yolov4，Yolov5，Yolov5，Yolov5，Yolov5，Yolov5，Yolov5，Yolov5，Yolov5，Yolov5，DETR，可变形的DETR，DINO-5SCALE-R50，VIT-ADAPTER-B和许多其他对象探测器的速度和准确性。此外，我们仅在不使用任何其他数据集或预先训练的权重的情况下从头开始训练Yolov7。源代码在https://github.com/wongkinyiu/yolov7中发布。

由于存储器和计算资源有限，部署在移动设备上的卷积神经网络（CNNS）是困难的。我们的目标是通过利用特征图中的冗余来设计包括CPU和GPU的异构设备的高效神经网络，这很少在神经结构设计中进行了研究。对于类似CPU的设备，我们提出了一种新颖的CPU高效的Ghost（C-Ghost）模块，以生成从廉价操作的更多特征映射。基于一组内在的特征映射，我们使用廉价的成本应用一系列线性变换，以生成许多幽灵特征图，可以完全揭示内在特征的信息。所提出的C-Ghost模块可以作为即插即用组件，以升级现有的卷积神经网络。 C-Ghost瓶颈旨在堆叠C-Ghost模块，然后可以轻松建立轻量级的C-Ghostnet。我们进一步考虑GPU设备的有效网络。在建筑阶段的情况下，不涉及太多的GPU效率（例如，深度明智的卷积），我们建议利用阶段明智的特征冗余来制定GPU高效的幽灵（G-GHOST）阶段结构。舞台中的特征被分成两个部分，其中使用具有较少输出通道的原始块处理第一部分，用于生成内在特征，另一个通过利用阶段明智的冗余来生成廉价的操作。在基准测试上进行的实验证明了所提出的C-Ghost模块和G-Ghost阶段的有效性。 C-Ghostnet和G-Ghostnet分别可以分别实现CPU和GPU的准确性和延迟的最佳权衡。代码可在https://github.com/huawei-noah/cv-backbones获得。

In recent years, object detection has achieved a very large performance improvement, but the detection result of small objects is still not very satisfactory. This work proposes a strategy based on feature fusion and dilated convolution that employs dilated convolution to broaden the receptive field of feature maps at various scales in order to address this issue. On the one hand, it can improve the detection accuracy of larger objects. On the other hand, it provides more contextual information for small objects, which is beneficial to improving the detection accuracy of small objects. The shallow semantic information of small objects is obtained by filtering out the noise in the feature map, and the feature information of more small objects is preserved by using multi-scale fusion feature module and attention mechanism. The fusion of these shallow feature information and deep semantic information can generate richer feature maps for small object detection. Experiments show that this method can have higher accuracy than the traditional YOLOv3 network in the detection of small objects and occluded objects. In addition, we achieve 32.8\% Mean Average Precision on the detection of small objects on MS COCO2017 test set. For 640*640 input, this method has 88.76\% mAP on the PASCAL VOC2012 dataset.

随着计算机愿景任务中的神经网络的不断发展，越来越多的网络架构取得了突出的成功。作为最先进的神经网络架构之一，DenSenet捷径所有特征映射都可以解决模型深度的问题。虽然这种网络架构在低MAC（乘法和累积）上具有优异的准确性，但它需要过度推理时间。为了解决这个问题，HardNet减少了特征映射之间的连接，使得其余连接类似于谐波。然而，这种压缩方法可能导致模型精度和增加的MAC和模型大小降低。该网络架构仅减少了内存访问时间，需要改进其整体性能。因此，我们提出了一种新的网络架构，使用阈值机制来进一步优化连接方法。丢弃不同卷积层的不同数量的连接以压缩阈值中的特征映射。所提出的网络架构使用了三个数据集，CiFar-10，CiFar-100和SVHN，以评估图像分类的性能。实验结果表明，与DENSENET相比，阈值可降低推理时间高达60％，并且在这些数据集上的硬盘相比，训练速度快高达35％的训练速度和20％的误差率降低。

虽然残留连接使训练非常深的神经网络，但由于其多分支拓扑而​​导致在线推断不友好。这鼓励许多研究人员在推动时没有残留连接的情况下设计DNN。例如，repvgg在部署时将多分支拓扑重新参数化为vgg型（单分支）模型，当网络相对较浅时显示出具有很大的性能。但是，RepVGG不能等效地将Reset转换为VGG，因为重新参数化方法只能应用于线性块，并且必须将非线性层（Relu）放在残余连接之外，这导致了有限的表示能力，特别是更深入网络。在本文中，我们的目标是通过在Resblock上的保留和合并（RM）操作等效地纠正此问题，并提出删除Vanilla Reset中的残留连接。具体地，RM操作允许输入特征映射通过块，同时保留其信息，并在每个块的末尾合并所有信息，这可以去除残差而不改变原始输出。作为一个插件方法，RM操作基本上有三个优点：1）其实现使其实现高比率网络修剪。 2）它有助于打破RepVGG的深度限制。 3）与Reset和RepVGG相比，它导致更好的精度速度折衷网络（RMNet）。我们相信RM操作的意识形态可以激发对未来社区的模型设计的许多见解。代码可用：https://github.com/fxmeng/rmnet。

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layerwise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on Ima-geNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ∼13× actual speedup over AlexNet while maintaining comparable accuracy.

在传统的对象检测框架中，从图像识别模型继承的骨干体提取了深层特征，然后颈部模块融合了这些潜在特征，以在不同的尺度上捕获信息。由于对象检测的分辨率比图像识别大得多，因此骨干的计算成本通常主导了总推断成本。这种沉重的背部设计范式主要是由于历史遗产将图像识别模型传输到对象检测时，而不是端到端的优化设计以进行对象检测。在这项工作中，我们表明这种范式确实导致了亚最佳对象检测模型。为此，我们提出了一种新型的重颈范式，长颈鹿，这是一个类似长颈鹿的网络，用于有效的对象检测。长颈鹿使用极轻的骨干和非常深的颈部模块，可同时同时在不同的空间尺度以及不同级别的潜在语义之间进行密集的信息交换。该设计范式允许检测器即使在网络的早期阶段，也可以在相同的优先级处理高级语义信息和低级空间信息，从而使其在检测任务中更有效。对多个流行对象检测基准的数值评估表明，长颈鹿在广泛的资源约束中始终优于先前的SOTA模型。源代码可在https://github.com/jyqi/giraffedet上获得。

The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1 st place in the COCO 2017 Challenge Instance Segmentation task and the 2 nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes. Code is available at https://github. com/ShuLiu1993/PANet.

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark datasets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and predictive performance.

现有的多尺度解决方案会导致仅增加接受场大小的风险，同时忽略小型接受场。因此，有效构建自适应神经网络以识别各种空间尺度对象是一个具有挑战性的问题。为了解决这个问题，我们首先引入一个新的注意力维度，即除了现有的注意力维度（例如渠道，空间和分支）之外，并提出了一个新颖的选择性深度注意网络，以对称地处理各种视觉中的多尺度对象任务。具体而言，在给定神经网络的每个阶段内的块，即重新连接，输出层次功能映射共享相同的分辨率但具有不同的接收场大小。基于此结构属性，我们设计了一个舞台建筑模块，即SDA，其中包括树干分支和类似SE的注意力分支。躯干分支的块输出融合在一起，以通过注意力分支指导其深度注意力分配。根据提出的注意机制，我们可以动态选择不同的深度特征，这有助于自适应调整可变大小输入对象的接收场大小。这样，跨块信息相互作用会导致沿深度方向的远距离依赖关系。与其他多尺度方法相比，我们的SDA方法结合了从以前的块到舞台输出的多个接受场，从而提供了更广泛，更丰富的有效接收场。此外，我们的方法可以用作其他多尺度网络以及注意力网络的可插入模块，并创造为SDA- $ x $ net。它们的组合进一步扩展了有效的接受场的范围，可以实现可解释的神经网络。我们的源代码可在\ url {https://github.com/qingbeiguo/sda-xnet.git}中获得。

深度学习技术在各种任务中都表现出了出色的有效性，并且深度学习具有推进多种应用程序（包括在边缘计算中）的潜力，其中将深层模型部署在边缘设备上，以实现即时的数据处理和响应。一个关键的挑战是，虽然深层模型的应用通常会产生大量的内存和计算成本，但Edge设备通常只提供非常有限的存储和计算功能，这些功能可能会在各个设备之间差异很大。这些特征使得难以构建深度学习解决方案，以释放边缘设备的潜力，同时遵守其约束。应对这一挑战的一种有希望的方法是自动化有效的深度学习模型的设计，这些模型轻巧，仅需少量存储，并且仅产生低计算开销。该调查提供了针对边缘计算的深度学习模型设计自动化技术的全面覆盖。它提供了关键指标的概述和比较，这些指标通常用于量化模型在有效性，轻度和计算成本方面的水平。然后，该调查涵盖了深层设计自动化技术的三类最新技术：自动化神经体系结构搜索，自动化模型压缩以及联合自动化设计和压缩。最后，调查涵盖了未来研究的开放问题和方向。

多层erceptron（MLP），作为出现的第一个神经网络结构，是一个大的击中。但是由硬件计算能力和数据集的大小限制，它一旦沉没了数十年。在此期间，我们目睹了从手动特征提取到带有局部接收领域的CNN的范式转变，以及基于自我关注机制的全球接收领域的变换。今年（2021年），随着MLP混合器的推出，MLP已重新进入敏捷，并吸引了计算机视觉界的广泛研究。与传统的MLP进行比较，它变得更深，但改变了完全扁平化以补丁平整的输入。鉴于其高性能和较少的需求对视觉特定的感应偏见，但社区无法帮助奇迹，将MLP，最简单的结构与全球接受领域，但没有关注，成为一个新的电脑视觉范式吗？为了回答这个问题，本调查旨在全面概述视觉深层MLP模型的最新发展。具体而言，我们从微妙的子模块设计到全局网络结构，我们审查了这些视觉深度MLP。我们比较了不同网络设计的接收领域，计算复杂性和其他特性，以便清楚地了解MLP的开发路径。调查表明，MLPS的分辨率灵敏度和计算密度仍未得到解决，纯MLP逐渐发展朝向CNN样。我们建议，目前的数据量和计算能力尚未准备好接受纯的MLP，并且人工视觉指导仍然很重要。最后，我们提供了开放的研究方向和可能的未来作品的分析。我们希望这项努力能够点燃社区的进一步兴趣，并鼓励目前为神经网络进行更好的视觉量身定制设计。

现代物体检测网络追求一般物体检测数据集的更高精度，同时计算负担也随着精度的提高而越来越多。然而，推理时间和精度对于需要是实时的对象检测系统至关重要。没有额外的计算成本，有必要研究精度改进。在这项工作中，提出了两种模块以提高零成本的检测精度，这是一般对象检测网络的FPN和检测头改进。我们采用规模注意机制，以有效地保险熔断多级功能映射，参数较少，称为SA-FPN模块。考虑到分类头和回归头的相关性，我们使用顺序头取代广泛使用的并联头部，称为SEQ-Head模块。为了评估有效性，我们将这两个模块应用于一些现代最先进的对象检测网络，包括基于锚和无锚。 Coco DataSet上的实验结果表明，具有两个模块的网络可以将原始网络超越1.1 AP和0.8 AP，分别为锚的锚和无锚网络的零成本。代码将在https://git.io/jtfgl提供。

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

Capturing feature information effectively is of great importance in vision tasks. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains on diverse deep learning vision tasks. However, the existing methods do not organically combined advantages of these valid ideas. In this paper, we propose a novel CNN architecture called GoogLe2Net, it consists of residual feature-reutilization inceptions (ResFRI) or split residual feature-reutilization inceptions (Split-ResFRI) which create transverse passages between adjacent groups of convolutional layers to enable features flow to latter processing branches and possess residual connections to better process information. Our GoogLe2Net is able to reutilize information captured by foregoing groups of convolutional layers and express multi-scale features at a fine-grained level, which improves performances in image classification. And the inception we proposed could be embedded into inception-like networks directly without any migration costs. Moreover, in experiments based on popular vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%) and Tiny Imagenet (70.54%), we obtain better results on image classification task compared with other modern models.

Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2.Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.

本文分析了面部检测体系结构的设计选择，以提高计算成本和准确性之间的效率。具体而言，我们重新检查了标准卷积块作为面部检测的轻质骨干结构的有效性。与当前的轻质体系结构设计的趋势（大量利用了可分开的卷积层）不同，我们表明，使用类似的参数大小时，大量通道绕的标准卷积层可以实现更好的准确性和推理速度。关于目标数据域的特征的分析，该观察结果得到了支持。根据我们的观察，我们建议使用高度降低的通道使用Resnet，与其他移动友好网络（例如Mobilenet-V1，-V2，-V3）相比，它具有高度效率。从广泛的实验中，我们表明所提出的主链可以以更快的推理速度替换最先进的面部检测器的主链。此外，我们进一步提出了一种最大化检测性能的新功能聚合方法。我们提出的检测器ERESFD获得了更宽的面部硬子子集的80.4％地图，该图仅需37.7 ms即可在CPU上进行VGA图像推断。代码将在https://github.com/clovaai/eresfd上找到。

由生物学进化的动机，本文通过类比与经过验证的实践进化算法（EA）相比，解释了视觉变压器的合理性，并得出了两者都具有一致的数学表述。然后，我们受到有效的EA变体的启发，我们提出了一个新型的金字塔饮食式主链，该主链仅包含拟议的\ emph {ea-ea-lase transformer}（eat）块，该块由三个残留零件组成，\ ie，\ emph {多尺度区域聚集}（msra），\ emph {global and local互动}（GLI）和\ emph {feed-forward Network}（ffn）模块，以分别建模多尺度，交互和个人信息。此外，我们设计了一个与变压器骨架对接的\ emph {与任务相关的头}（TRH），以更灵活地完成最终信息融合，并\ emph {reviv} a \ emph {调制变形MSA}（MD-MSA），以动态模型模型位置。关于图像分类，下游任务和解释性实验的大量定量和定量实验证明了我们方法比最新方法（SOTA）方法的有效性和优越性。 \例如，我们的手机（1.8m），微小（6.1m），小（24.3m）和基地（49.0m）型号达到了69.4、78.4、83.1和83.9的83.9 TOP-1仅在Imagenet-1 K上接受NAIVE训练的TOP-1食谱; Eatformer微型/小型/基本武装面具-R-CNN获得45.4/47.4/49.0盒AP和41.4/42.9/44.2掩膜可可检测，超过当代MPVIT-T，SWIN-T，SWIN-T和SWIN-S，而SWIN-S则是0.6/ 1.4/0.5盒AP和0.4/1.3/0.9掩码AP分别使用较少的拖鞋；我们的Eatformer-small/base在Upernet上获得了47.3/49.3 MIOU，超过Swin-T/S超过2.8/1.7。代码将在\ url {https：//https：//github.com/zhangzjn/eatformer}上提供。