論文筆記:Bag of Freebies for Training Object Detection Neural Networks
這篇論文看起來像是一種總結類文章，文中並沒有提出新的結構模型之類，但是對於以前的一些做法進行了整合並應用於baseline中。也就是文中一再強調的「Bag of Freebies」，即在不改變模型結構並且不改變損失函數的情況下，不犧牲前向傳播時間，將mAP提升近五個點。 論文主要貢獻如下： 首次系統地評估了不同目標檢測流程中應用的多種訓練啓發式方法，爲未來的研究提供了有價值的實踐指導。 提出了一種
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相關文章
- 1. Bag of Freebies for Training Object Detection Neural Networks
- 2. 論文筆記:Bag of Freebies for Training Object Detection Neural Networks
- 3. Bag of Freebies for Training Object Detection Neural Networks 論文筆記
- 4. 論文解讀-Bag of Freebies for Training Object Detection Neural Networks
- 5. 《Bag of Freebies for Training Object Detection Neural Networks》論文理解
- 6. 論文閱讀：Bag of Freebies for Training Object Detection Neural Networks
- 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
- 9. 目標檢測調優技巧：《Bag of Freebies for Training Object Detection Neural Networks》論文筆記
- 10. 目標檢測訓練優化Tricks：《Bag of Freebies for Training Object Detection Neural Networks》