[Caffe] Interpretation of Alexnet model

On the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model won the 2012-term championship. In order to study the application of the CNN type DL network model in image classification, we can not escape the research alexnet, which is the classic model of CNN in image classification (after the DL fires up).

In the DL open source Implementation Caffe Model sample, it also gives the alexnet of the recurrence, the specific network configuration file is as follows Https://github.com/BVLC/caffe/blob/master/models/bvlc_ Reference_caffenet/train_val.prototxt:

next This article will step by step to the network configuration structure of the various layers of the detailed interpretation (training phase):

1. CONV1 phase DFD (Data flow diagram):

2. Conv2 phase DFD (Data flow diagram):

3. Conv3 phase DFD (Data flow diagram):

4. CONV4 phase DFD (Data flow diagram):

5. CONV5 phase DFD (Data flow diagram):

6. Fc6 phase DFD (Data flow diagram):

7. FC7 phase DFD (Data flow diagram):

8. Fc8 phase DFD (Data flow diagram):

Various layers of operation more explanations can be referred to http://caffe.berkeleyvision.org/tutorial/layers.html

From the process of calculating the data flow of the model, the model parameters are probably 5kw+.

[Caffe] Interpretation of Alexnet model