May 16, 2024 · YOLOv5是一种快速且精确的目标检测算法，采用单阶段检测方式，包括CSPDarknet53主干网络、FPN特征金字塔和优化的预测头。其特点在于高效性、高精度和易用性，适用于多种检测任务。网络结构中的C3模块增加了深度和感受野，SPP模块处理多尺度信息，而FPN则融合不同层级的特征，提高检测性能。 Jul 30, 2024 · Abstract This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. The backbone is CSPDarknet53, a modification of Darknet, and the features include SPPF, PANet, AutoAnchor, EMA, and more. Jul 3, 2024 · YOLOv5 introduced significant innovations such as the CSPDarknet backbone and Mosaic Augmentation, balancing speed and accuracy. YOLOv5 uses a CSP (Cross Stage Partial) backbone based on the CSPDarknet architecture: Initial feature extraction with Focus module or Conv Series of CSP blocks (C3 modules) with downsampling Produces feature maps at different resolutions Neck The neck connects the backbone to the detection Download scientific diagram | Architecture of YOLOv5, including three main parts: backbone, neck and head. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explored in detail. 23 Finish network slimming pruning YOLOv5 series Finish network SFP pruning YOLOv5 series Finish network FPGM pruning YOLOv5 series Finish network slimming pruning YOLOv5 series YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite. For YOLOv5, the backbone is designed using the CSPDarknet53 structure, a modification of the Darknet architecture used in previous versions. 2021. Additionally, the study YOLOv5's architecture consists of three main parts: Backbone: This is the main body of the network. Ultralytics’ YOLOv5 (2020) popularized a PyTorch-native, modular toolchain that eased adaptation to segmentation, classification, and edge deployment. 12. Subsequent community releases (YOLOv6, YOLOv7) integrated parameter-efficient modules and transformer-inspired blocks to push accuracy while maintaining real-time inference [17, 18]. YOLOv6 introduced a fully decoupled head architecture, allowing specialization of features. . All the YOLOv5 models are composed of the same 3 components: CSP-Darknet53 as a backbone, SPP and PANet in the model neck and the head used in YOLOv4. YOLOv8 built upon this foundation with enhanced feature extraction and anchor-free detection, improving versatility and performance. We propose Transformer-GELAN, a PGI-aware hybrid backbone module that integrates a lightweight dual block Swin Transformer [39] into the final stage of YOLOv9c’s backbone, replacing RepNCSPELAN4. from publication: A robust bridge rivet identification method using deep learning and Jul 3, 2024 · YOLOv5 introduced significant innovations such as the CSPDarknet backbone and Mosaic Augmentation, balancing speed and accuracy. 27 change backbone to shufflenetv2 change backbone to efficientnetv2 change backbone to shufflenetv2 change backbone to efficientnetv2 2022. Contribute to ultralytics/yolov5 development by creating an account on GitHub. Neck: This part connects the backbone and the head. The paper reviews the model’s performance across various metrics and hardware platforms. YOLOv8 transitioned to anchor-free prediction, while YOLOv9 merged decoupling with programmable gradient routing for dynamic task emphasis. 05. In YOLOv5, SPPF (Spatial Pyramid Pooling - Fast) and PANet (Path Aggregation Network) structures are Apr 18, 2025 · Backbone The backbone is responsible for extracting features from the input image at different scales. Aug 4, 2025 · Until YOLOv5, detection heads were largely coupled—jointly predicting class scores and bounding box regressions. 3 days ago · Learn about the backbone structure, data augmentation techniques, training strategies, and additional features of YOLOv5, a powerful object detection algorithm. Network architecture for YOLO v5 [2] CSP-Darknet53 YOLOv5 uses CSP-Darknet53 as its backbone.

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