YOLOv7 Object Detection Paper Explanation and Inference

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YOLOv7 Object Detection Paper Explanation and Inference

YOLOv7 Paper Explanation: Object Detection and YOLOv7 Pose

“YOLOv7 is the new state-of-the-art object detector in the YOLO family. According to the paper, it is the fastest and most accurate real-time object detector to date. According to the YOLOv7 paper, the best model scored 56.8% Average Precision (AP), which is the highest among all known object detectors. The speed ranges from 5-160 FPS for various models (Available in the¬†YOLOv7 GitHub¬†repository). Compared to the base models, YOLOv7 has reduced number of parameters to 40% and computation to 50%.

This blog post contains simplified YOLOv7 paper explanation and inference tests. We will go through the YOLOv7 GitHub repository and test inference. We will also see how YOLOv7 compares with other object detectors of the YOLO family…”

Source: learnopencv.com/yolov7-object-detection-paper-explanation-and-inference

August 3, 2022
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