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29 de nov. de 2022 · This article explains several performance comparison between different YOLO object detection models. These include YOLOv5, YOLOv6, and YOLOv7.
21 de nov. de 2023 · YOLOv7 vs YOLOv5 Comparison. Compared to YOLOv5-N, YOLOv7-tiny is 127 FPS faster and 10.7% more accurate on AP. The version YOLOv7-X achieves 114 FPS inference speed compared to the comparable YOLOv5-L with 99 FPS, while YOLOv7 achieves a better accuracy (higher AP by 3.9%).
25 de nov. de 2022 · Shortly after its publication, YOLOv7 is the fastest and most accurate real-time object detection model for computer vision tasks.
12 de nov. de 2023 · If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L.
12 de ene. de 2023 · YOLO v5, v7, and v8 are the latest versions of the YOLO framework, and in this blog post, we will compare their performance on the NVIDIA Jetson AGX Orin 32GB platform, the most powerful embedded AI computer, and on an RTX 4070 Ti desktop card.
4 de ene. de 2024 · YOLOv7 infers faster and with greater accuracy than its previous versions (i.e. YOLOv5), pushing the state of the art in object detection to new heights. The evaluation of YOLOv7 models show that they infer faster (x-axis) and with greater accuracy (y-axis) than comparable realtime object detection models.
24 de jul. de 2023 · The YOLO algorithm tries to reframe object detection as a single regression problem, including image pixels, to class probabilities, and bounding box coordinates. Hence, the algorithm has to look at the image only once to predict & locate the target objects in the images.
