A Better, Faster, and Stronger Object Detector (YOLOv2).Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1).This lesson is the 6th part in our 7-part series on YOLO: And the purpose of this experiment was to motivate people to leverage the YOLOv4 model’s power without thinking much about the type of GPU since it performs the best even on a conventional GPU which makes this paper even more exciting and unique. (2020) did extensive experiments across different GPU architectures and showed that YOLOv4 outperformed all the other object detection network architectures in terms of speed and accuracy. And that’s what this paper does in the best way!īochkovskiy et al. But the most challenging part is combining these different features that complement each other, thus providing an optimal and accurate object detection model. We believe that YOLOv4 results from many experiments and studies that combine various small novel techniques that improve the Convolutional Neural Network accuracy and speed. We would build the intuition by comparing this network with the previous work on object detection. In this tutorial, you will learn all about YOLOv4 from a research perspective as we will dive deeper into the workings of this accurate and fast object detection network. Configuring the Darknet Framework and Running Inference with Pretrained YOLOv4 COCO ModelĪchieving Optimal Speed and Accuracy in Object Detection (YOLOv4).Influence of BoF and Mish Activation on the Backbone.DropBlock Regularization (Backbone → BoF).Spatial Attention Module (Detector → BoS).Path Aggregation Network (Detector → BoS).Modified SPP: Spatial Pyramid Pooling (Detector → BoS).CSP: Cross-Stage Partial Connection (Backbone → BoS).Self-Adversarial Training (Detector → BoF).Mosaic Data Augmentation (Backbone → BoF).CutMix Data Augmentation (Backbone → BoF).Having Problems Configuring Your Development Environment?.Configuring Your Development Environment.Achieving Optimal Speed and Accuracy in Object Detection (YOLOv4).

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