Python影像辨識筆記(十六):使用PyTorch建立YOLOv3模型

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參考資料:

[ 0 ] ultralytics/yolov3

[ 1 ] How to implement a YOLO (v3) object detector from scratch in PyTorch

從該系列的文章中發現,在PyTorch上建立YOLOv3是透過python去抓取yolov3.cfg神經網路架構資料成dict型態Part 1 : Understanding How YOLO works (主要是YOLOv3原理的介紹,沒有寫到程式碼)Part 2 (This one): Creating the layers of the network architecture (從yolov3.cfg檔案抓出網路架構資料成dict型態)Part 3 : Implementing the the forward pass of the network(建立Network)Part 4 : Objectness Confidence Thresholding and Non-maximum Suppression (處理閾值和NMS)Part 5 : Designing the input and the output pipelines(進行Image or Video的Detection)

[ 2 ] 匯總| Pytorch YOLO 項目推薦建議收藏學習

[ 3 ] [深度学习框架] PyTorch 常用代码段总结

[4] 利用PyTorch自己动手从零实现YOLOv3

[ 5 ] PyTorch 手册

[ 6 ] PYTORCH CHEAT SHEET

[ 7 ] deep-learning-v2-pytorch

[ 8 ] YOLO-Reproduce-Summary

[ 9 ] PyTorch Computer Vision Cookbook

[ 10 ]【从零开始学习YOLOv3】7. 教你在YOLOv3模型中添加Attention机制

[ 11 ] YOLOv3架構解析

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Machine Learning / Deep Learning / Python / Flutter cakeresume.com/yanwei-liu

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