深度學習【51】物體檢測：PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
這篇論文在coco數據集中取得了第二名的成績。其目標是在取得state-of-the-art的正確率情況下，最小化計算量。主要工作是優化前面的特徵抽取網絡，設計原則是通道數要少，但是網絡更深。該論文的網絡設計主要點爲：1、concatencated ReLU；2、inception模塊；3、HyperNet，加入不同尺度特徵圖融合。 C.ReLU C.ReLU的結構如下： 其前向傳播過程爲：輸入數
相關文章
- 1. 論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 2. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 3. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
- 4. 【PVANet】《PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection》
- 5. 【目標檢測】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
- 6. 論文筆記: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- 7. Deep Neural Networks for Object Detection
- 8. 深度學習【53】物體檢測：Feature Pyramid Networks for Object Detection
- 9. Deep Neural Networks for Object Detection論文翻譯
- 10. (37)[NIPS13] Deep Neural Networks for Object Detection
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相關文章
- 1. 論文閱讀--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 2. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 3. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
- 4. 【PVANet】《PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection》
- 5. 【目標檢測】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
- 6. 論文筆記: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- 7. Deep Neural Networks for Object Detection
- 8. 深度學習【53】物體檢測：Feature Pyramid Networks for Object Detection
- 9. Deep Neural Networks for Object Detection論文翻譯
- 10. (37)[NIPS13] Deep Neural Networks for Object Detection