對yolo v2網路層數的理解
1.darknet_19的網路結構
| 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 |
//卷積，增加通道數 batch_normalize=1 |
| 1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32 |
//池化，縮小特徵圖片大小 size=2 |
| 2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 |
//卷積，增加通道數 [convolutional] |
| 3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64 |
//池化，縮小特徵圖片大小 [maxpool] |
4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 //卷積，增加通道數
5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 //1*1的卷積，在不改變圖片尺寸的基礎上改變圖片通道數，
6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
25 route 16
26 conv 64 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 64
27 reorg / 2 26 x 26 x 64 -> 13 x 13 x 256
28 route 27 24
29 conv 1024 3 x 3 / 1 13 x 13 x1280 -> 13 x 13 x1024
30 conv 60 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 60
31 detection