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Lecture 9: CNN Architectures

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Fei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 2017Fei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20171Lecture 9:CNN ArchitecturesFei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20172AdministrativeA2 due Thu May 4Midterm: In-class Tue May 9. Covers material through Thu May 4 session: Tue June 6, 12-3pmFei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 2017Last time: Deep learning frameworks3Caffe (UC Berkeley)Torch (NYU / Facebook)Theano (U Montreal)TensorFlow (Google)Caffe2 (Facebook)PyTorch (Facebook)CNTK (Microsoft)Paddle (Baidu)MXNet (Amazon)Developed by U Washington, CMU, MIT, Hong Kong U, etc but main framework of choice at AWSAnd Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20174(1)Easily build big computational graphs(2)Easily compute gradients in computational graphs(3)Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc)Last time.

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners Deeper Networks. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - May 2, 2017 Case Study: VGGNet 26 3x3 conv, 128 Pool 3x3 conv, 64 3x3 conv, 64 Input 3x3 conv, 128 Pool 3x3 conv, 256 3x3 conv, 256 Pool 3x3 conv, 512 3x3 conv, 512 Pool 3x3 conv, 512 3x3 conv, 512 Pool FC 4096

  Visual

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