Scaling Tiny Models for Low-End Devices 作者接着针对小模型进行分析,提出了要注意的几点: 考虑计算量:FLOPs 是浮点计算数量,是考虑网络结构中计算量的重要指标,作者在 computation block 部分使用了 VoVNet 中的 OSA Module 降低 FLOPs(笔记链接); 考虑内存读取:MAC 是用来衡量内存读取的指标,这里根据 memory acc...
由于 YOLOv7-tiny 是一个面向边缘端 GPU 的架构,因此它将使用 ReLU 作为激活函数。对于其他模型,我们使用 SiLU 作为激活函数。我们将在附录中详细描述每个模型的比例因子。 表2:最先进的实时目标检测器的比较 1.我们的 FLOPs 是由矩形输入分辨率计算的,如 640×640 或 1280×1280。2.我们的推理时间是通过使用...
YOLOv7-tiny(边缘GPU)、YOLOv7(普通GPU)、YOLOv7-W6(云GPU) 缩放版本 YOLOv7-X:基于YOLOv7,对neck部分进行stage缩放+使用新的模型缩放法对整个模型部分进行depth和width缩放 YOLOv7-E6:基于YOLOv7-W6,使用新的模型缩放法进行了depth缩放 YOLOv7-D6:基于YOLOv7-W6,使用新的模型缩放法进行了depth和width...
我这里选择yolov7-tiny模型,在cmd中进入应用程序darknet(windwows为darknet.exe)所在目录,windows训练使用如下命令: darknet detector train E:/yolov7_tiny.data E:/yolov7-tiny.cfg E:/yolov7-tiny.weights linux命令为 ./darknet detector train E:/yolov7_tiny.data E:/yolov7-tiny.cfg E:/yolov...
I have a question regarding YOLOv7-tiny's parameters (after just calling v7), which are larger than YOLOv8n, but the FLOPs are less than YOLOv8n. Additionally, YOLOv7-tiny's speed is slower than YOLOv8. Could someone explain these results? I would appreciate any insights from knowledgea...
模型图像大小 (像素)mAPval 50-95CPU ONNX 速度 (毫秒)A100 TensorRT 速度 (毫秒)参数数量 (百万)FLOPs (十亿) YOLOv5nu 640 34.3 73.6 1.06 2.6 7.7 YOLOv8n 640 37.3 80.4 0.99 3.2 8.7 YOLOv6N 640 37.5 - - 4.7 11.4 YOLOv7-tiny 640 37.4 - - 6.01 13.1 (2)度量指标: F1-Score:F1-Score...
Compared with baseline YOLOv7‐tiny, the lightweight GP‐YOLO has similar parameters and FLOPs, but the mAP@0.5:0.95 is increased by 2.1%, and the Recall is increased by 2.4%. 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.Wang, Ling...
(3) We propose a model DC-YOLO for field plant target detection, which is an optimized algorithm based on YOLOv7-tiny. After experiments, this model outperforms other mainstream lightweight object detection models in our task. The rest of the paper is structured as follows, with the “Metho...
The experimental results show that the improved YOLOv7-tiny achieves an average precision of 96.5% for vegetable detection, with a frame rate of 89.3 fps, Params of 8.2 M, and FLOPs of 10.9 G, surpassing the original YOLOv7-tiny in both detection accuracy and speed. The image segmentation...
它是在 YOLOv5 的基础上进行改进和优化得到的，它结合了众多优秀的先进思想，共有 YOLOv7-tiny，YOLO...