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You don't put batch normalization or dropout layers after the last layer, it will just "corrupt" your predictions. They are intended to be used only within the network, to help it converge and avoid overfitting. BTW even if your fully connected layer's output is always positive, it would have positive and negative outputs after batch normalization. ...


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In any case anyone is struggling with the same problem. It seems that they were simply typos in the original paper. I have downloaded the author's framework Darknet, as well as the configuration and weight files for YOLOv1. Then, the architecture can be tested with one sample image using this command: ./darknet yolo test cfg/yolov1/yolo.cfg yolov1.weights ...


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