R-FCN-3000, Segment Every Thing, YOLO9000
本文主要關注分類，檢測和分割等任務訓練數據的解耦，非常有趣。 R-FCN-3000 R-FCN-3000 obtains an mAP of 34.9% on the ImageNet detection dataset and outperforms YOLO 9000 by 18% while processing 30 images per second. 主要思路如上圖，第一行將原來rfc
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相關文章
- 1. Learning to Segment Every Thing筆記
- 2. 【極簡筆記】Learning to Segment Every Thing
- 3. Learning to Segment Every Thing算法筆記
- 4. Learning to Segment Every Thing Ronghang論文學習
- 5. 【論文學習筆記】Learning to Segment Every Thing (2018_CVPR)
- 6. 【論文學習記錄】Learning to Segment Every Thing
- 7. [深度學習論文筆記][arxiv 1711]Learning to Segment Every Thing
- 8. YOLO9000
- 9. First thing first
- 10. YOLO9000:Better, Faster, Stronger