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A detailed walkthrough of the YOLOv3 paper covering the key improvements over YOLOv2: the Darknet-53 backbone with residual blocks, multi-scale detection heads (13×13, 26×26, 52×52), multilabel classification using sigmoid instead of softmax, and binary cross-entropy loss. Includes a full PyTorch implementation from scratch, building the Convolutional block, Residual block, Darknet-53, DetectionHead, and the complete YOLOv3 model with feature concatenation via upsampling. Experimental results show YOLOv3 achieves state-of-the-art inference speed while maintaining competitive mAP against RetinaNet.

YOLOv3 Paper Walkthrough: Even Better, But Not That Much