Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca D
识别葡萄的一种虫害，比较了传统SIFT和深度学习分类，最后还作了目标检测网络
分类用的 MobileNet，目标检测 RetinaNet性能
MobileNet 是将传统深度可分离卷积分红了两步，深度卷积和逐点卷积，性能基本不受影响状况下，下降计算量和参数量学习
RetinaNet 主要提出 focal loss，用于处理目标检测时正负样本不均衡问题，FL的做用以下blog
网络结构就是 FPN + sub-network + FL深度学习
FPN是图像金字塔网络，对不一样scale的图像进行融合和预测，主要用于目标检测，原理感受和unet差很少原理
相关文章
- 1. [解读] Deep Clustering for Unsupervised Learning of Visual Features
- 2. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 3. 【译】The challenge of verification and testing of machine learning
- 4. 【学习】Deep Learning for Deepfakes Creation and Detection
- 5. Deep Clustering for Unspervised Learning of Visual Features
- 6. Summary of defect detection algorithms based on deep learning
- 7. ASLFeat: Learning Local Features of Accurate Shape and Localization &&2020
- 8. 论文：Automated Vehicle Detection and Classification: Models, Methods, and Techniques翻译（四）
- 9. 【文献学习】Power of Deep Learning for Channel Estimation and Signal Detection in OFDM
- 10. Some methods of deep learning and dimensionality reduction
- 更多相关文章...
- • XSLT
- • XSLT
- • RxJava操作符（七）Conditional and Boolean
- • 为了进字节跳动，我精选了29道Java经典算法题，带详细讲解
相关标签/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
欢迎关注本站公众号,获取更多信息
相关文章
- 1. [解读] Deep Clustering for Unsupervised Learning of Visual Features
- 2. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 3. 【译】The challenge of verification and testing of machine learning
- 4. 【学习】Deep Learning for Deepfakes Creation and Detection
- 5. Deep Clustering for Unspervised Learning of Visual Features
- 6. Summary of defect detection algorithms based on deep learning
- 7. ASLFeat: Learning Local Features of Accurate Shape and Localization &&2020
- 8. 论文：Automated Vehicle Detection and Classification: Models, Methods, and Techniques翻译（四）
- 9. 【文献学习】Power of Deep Learning for Channel Estimation and Signal Detection in OFDM
- 10. Some methods of deep learning and dimensionality reduction