深度學習論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
Abstract mAP: mean average precision，平均準確度 咱們的方法結合兩個關鍵的看法：web 第一：採用高容量的卷積神經網絡來從上到下的進行region proposal，從而實現定位和分割物體。 當標記的訓練數據稀缺時，能夠先對輔助數據集（任務）進行受監督的預訓練， 隨後是基於域進行特定調整，產生顯着的性能提高。 Introduction 關於各類視覺識別任務的上一
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相關文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 【論文閱讀筆記】Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. 論文：Rich feature hierarchies for accurate object detection and semantic segmentation 個人筆記
- 5. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)
- 6. 論文學習《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 7. 【論文學習】Rich feature hierarchies for accurate object detection and semantic segmentation
- 8. RCNN學習筆記(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. R-CNN論文翻譯Rich feature hierarchies for accurate object detection and semantic segmentation