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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
6.
Bag of Freebies for Training Object Detection Neural Networks 論文筆記
7.
Bag of Freebies for Training Object Detection Neural Networks學習筆記
8.
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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目標檢測優化技巧(Bag of Freebies for Training Object Detection Neural Networks)
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