深度網絡推理加速（Towards Lightweight Convolutional Neural Networks for Object Detection）
摘要：本文研究目標是少類別實時目標檢測，研究了在保留較高檢測率的前提下最大程度的減少模型大小，最終實現了在CPU上的實時檢測。本文的推理加速機制和量化壓縮等方法是能夠並行的。算法 1， 經過增大feature map的尺寸，減少通道數能夠獲得準確率且快的檢測模型，這麼作的依據是對於實際的不多類別的目標檢測問題，用於解決多類別分類問題的網絡中不少通道是冗餘的。網絡 2， 經過在大的預訓練模型上簡
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相關文章
- 1. 論文筆記: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- 2. Convolutional Neural Networks（week 3）---Object detection
- 3. 【PVANet】《PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection》
- 4. 【目標檢測】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
- 5. Deep Neural Networks for Object Detection
- 6. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 7. Convolutional Neural Network - Object Detection
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- 9. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
- 10. LCDet Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems