目前来看,该算法与YOLOv5s对比来看,相差不大,可能综合整体来说稍微有所提升,没有想象中那样提高很大的精度,有很惊人的表现。 个人建议:有需要发论文的朋友,可以改YOLOv7网络,之前也分享很多改进方法也可以用到V7,可以在其他数据集上进行尝试。但是网络结构比较多,比较难画图,还不如YOLOv5好用,个人倾向于用V7来...
试验结果表明,SLP-YOLOv7-tiny模型整体识别精准度、召回率、平均精度均值mAP0.5(IOU阈值为0.5时的平均精度)、mAP0.5～0.95(IOU阈值从0.5到0.95之间的平均精度)分别为95.9%、94.6%、98.0%、91.4%,与改进前YOLOv7-tiny相比,分别提升14.7...
Compared with YOLOv5n, YOLOv5s, YOLOv5m, YOLOv7, Yolov7-Tiny, Faster-RCNN and SSD target detection models, mAP0.5 improved by 2.0, 1.6, 2.0, 2.2, 20.2, 6.1 and 5.3 percentage points respectively. The computing capacity was only 31.5%, 10.6%, 4.9%, 4.3% and...
yolov5s 256 7.0 16.0 14.5 92.7 90.3 94.8 55.6 0.705 13.0 yolov5m 128 20.9 48.3 42.3 93.1 89.4 94.2 55.0 1.0098 16.8 yolov5l 64 46.2 108.3 92.9 93.1 88.8 94.3 55.0 1.751 25.6 yolov5x 32 86.2 204.8 173.2 92.6 89.4 94.5 55.4 3.068 40.4 – – – – – – – – – – – yolov7-...
From the experimental results, we can see that the detection performance of YOLOv7-tiny is slightly higher than that of YOLOv5s in our task. on mAP@0.5, YOLOXs is similar to YOLOv7-tiny, while mAP@0.5:0.95 is higher than YOLOv7-tiny, but with a higher computational complexity. YOLO...
进行了改进.实验表明,改进后的YOLOv7-Tiny和YOLOv5s-v6.1分别在快速检测和高精度识别方面表现优异.最终,本文选择了精度更高的改进YOLOv5s-v6.1作为主要目标检测... 赵鹏飞 - 《浙江农林大学》 被引量: 0发表: 2024年 Enhanced Zero-Shot YOLOv10 for Multi-Class Tiny-Object Detection of Steel Surface Defe...
Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Question I attempted to compare YOLOv8n, YOLOv7-tiny, and YOLOv5s/6, using a custom dataset that classifies a single class. The result...
The comparison with Faster RCNN, SSD, YOLOv4, YOLOv5s, YOLOv8s, and other mainstream target detection models shows that this method greatly solves the field environment. The problem of small spots and fuzzy edges of photographed rice diseases provides a basis for intelligent management of ...
The maximum detection accuracy of the YOLOv7-Tiny-NET model is 0.837, and the model size of the YOLOv7-Tiny-NET model is reduced by 3.52MB and 37.8 f/s increases the detection speed compared with SAG-YOLOv5s. The maximum success rate of the autonomous decision-making algorithm of UAV ...
Compared with SSD, Faster RCNN, YOLOv3, YOLOv4, and YOLOv5s models, the mAP of the YOLOv7-Tiny model increased by 14.2%, 1.52%, 3.15%, 3.01%, and 2.6%. The recognition speed increased by 79.3%, 92.9%, 80.4%, 58.8%, and 69.6%. The number of parameters decreased by 90%, 89.7...