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Bag of Freebies for Training Object Detection Neural Networks
時間 2020-12-23
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李沐等將目標檢測絕對精度提升 5%,不犧牲推理速度。 目標檢測無疑是計算機視覺領域最前沿的應用之一,吸引了各個領域諸多研究者的目光。最前沿的檢測器,包括類似 RCNN 的單(SSD 或 YOLO)或多階神經網絡都是基於圖像分類骨幹網絡,如 VGG、ResNet、Inception 或 MobileNet 系列。 然而,由於模型容量和訓練複雜度相對較高,目標檢測受到的關注相對較少,從最近的訓練微調研
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相關文章
1.
Bag of Freebies for Training Object Detection Neural Networks
2.
論文解讀-Bag of Freebies for Training Object Detection Neural Networks
3.
論文筆記:Bag of Freebies for Training Object Detection Neural Networks
4.
《Bag of Freebies for Training Object Detection Neural Networks》論文理解
5.
論文閱讀:Bag of Freebies for Training Object Detection Neural Networks
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Bag of Freebies for Training Object Detection Neural Networks 論文筆記
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Bag of Freebies for Training Object Detection Neural Networks學習筆記
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目標檢測tricks:Bag of Freebies for Training Object Detection Neural Networks
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目標檢測訓練優化Tricks:《Bag of Freebies for Training Object Detection Neural Networks》
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