From: Understanding deep learning via backtracking and deconvolution
ConvNet configuration | ||||
---|---|---|---|---|
Input | 224 × 224 × 3 | |||
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Maxpool | Filter | Stride | ||
2 × 2 | 2 | |||
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Maxpool | Filter | Stride | ||
2 × 2 | 2 | |||
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Conv | Receptive field | Stride | Padding | Feature map |
3 × 3 | 1 | 1 | 32 | |
Maxpool | Filter | Stride | ||
2 × 2 | 2 | |||
FC | 1024 | |||
FC | 512 | |||
FC | 256 | |||
FC | No. of classes | |||
Softmax |