RCNN(Regions with Convolutional Neural Network Feature)
背景知識 在目標檢測評價中,有一個參數定位準確率,即IOU,IOU計算公式如下: 簡單來說,IOU就是模型預測的bounding box與標記bounding box的重合率. 通常我們預測的bounding box是無法與標記bounding box完全重合的,例如下面幾種情況: 因此可以通過求兩個bounding box的重合率即IOU,並設定一個閾值t0,並認爲IOU值大於t0的候選框爲
相關文章
- 1. 《ImageNet Classification with Deep Convolutional Neural Network》
- 2. Convolutional Neural Network - Object Detection
- 3. NR-SIQA Using Convolutional Neural Network for Adaptive Feature Extraction
- 4. Convolutional Neural Network
- 5. Convolutional Neural Network Architecture
- 6. CNN(Convolutional Neural Network)
- 7. Convolutional Neural Network（CNN）tips
- 8. Transposed convolution & Fully Convolutional Neural Network
- 9. 【UFLDL】Exercise: Convolutional Neural Network
- 10. Computing the Stereo Matching Cost with a Convolutional Neural Network
- 更多相關文章...
- • XSLT
- • PHP curl_version函數 - PHP參考手冊
- • Flink 數據傳輸及反壓詳解
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 《ImageNet Classification with Deep Convolutional Neural Network》
- 2. Convolutional Neural Network - Object Detection
- 3. NR-SIQA Using Convolutional Neural Network for Adaptive Feature Extraction
- 4. Convolutional Neural Network
- 5. Convolutional Neural Network Architecture
- 6. CNN(Convolutional Neural Network)
- 7. Convolutional Neural Network（CNN）tips
- 8. Transposed convolution & Fully Convolutional Neural Network
- 9. 【UFLDL】Exercise: Convolutional Neural Network
- 10. Computing the Stereo Matching Cost with a Convolutional Neural Network