《Very deep convolutional networks for large-scale image recognition》阅后笔记
VGGNet是牛津大学计算机视觉组和DeepMind公司共同研发一种深度卷积网络，并且在２０１４年在ILSVRC比赛上获得了分类项目的第二名和定位项目的第一名。 论文主要研究目的： 通过使用具有非常小（3×3）卷积核的架构对深度增加的网络进行全面评估。探究在大规模图像识别任务中，卷积网络深度对模型精确度有何影响。 为了衡量不同深度对精度造成的影响，所有的ConvNet层配置都采用相
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相关文章
- 1. 论文阅读笔记--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 2. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 3. VGG——Very deep convolutional networks for large-scale image recognition
- 4. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 5. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 6. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 7. VGG: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 8. 论文笔记：Very Deep Convolutional Networks for Large-Scale Image Recognition
- 9. 论文笔记《Very Deep Convolutional Networks for Large-Scale Image Recognition》
- 10. VGGNet 《VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION》学习笔记