The results are shown in the table below: 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?
加粗的表示最优结果。 算法batch_sizeparam/MillionFLOPs/Gweight_size/MBP/%R/%mAP50/%mAP50-95/%train_time/hSpeed/ms yolov5n 256 1.7 4.3 3.9 91.9 88.1 93.9 53.2 0.682 11.0 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...
FLOPs represent the amount of computation. M and N are the number of input and output channels, respectively. FLOPs=𝐷𝐾×𝐷𝐾×𝑀×𝑁FLOPs=DK×DK×M×N (1) when employing depthwise separable convolution operations; the associated computational cost is outlined in Equation (2). ...
Finally, a lightweight method based on PModule is proposed, on this basis, a PConv bottleneck is designed to reduce the FLOPs and enhance the feature extraction. Experiments on a self‐made Industrial Pedestrian Data set show that before lightweight, the proposed algorithm achieves a...
In addition, FLOPs is used to indicate the number of floating-point operations to measure the complexity of the model, and Params is used to indicate the total number of parameters to evaluate the size of the model, and frames per second (FPS) denotes the number of frames per second that...
[0], input_shape[1]).to(device) flops, params = profile(m.to(device), (dummy_input, ), verbose=False) #---# # flops * 2是因为profile没有将卷积作为两个operations # 有些论文将卷积算乘法、加法两个operations。此时乘2 # 有些论文只考虑乘法的运算次数,忽略加法。此时不乘2 # 本代码...
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...
Parameters, FLOPs, and Size measure the model’s complexity, while FPS measures the inference speed per image, with higher FPS indicating faster inference, beneficial for edge deployment. The formulae for P, R, AP, mAP are Equations (5)–(8). Precision=𝑇𝑃𝑇𝑃+𝐹𝑃Precision=...
flops for FLOPs in G input for model input shape acc_metrics means Imagenet Top1 Accuracy for recognition models, COCO val AP for detection models inference_qps for T4 inference query per second with batch_size=1 + trtexec extra means if any extra training info. from keras_cv_attention_mode...
Specifically, our work reduces the usage of digital signal processing (DSP) units by 90% and it saves up to 60% of flip-flops compared to state-of-the-art designs, while achieving competitive usage of block RAM and look-up tables. Additionally, the achieved design latency of 15ms is ...