-
Augmentations for faces:
- Random Brightness
- Random Contrast
- Random Gamma
- Random Saturation
- Random Hue
- Random Rotation
-
Augmentations for landmarks:
- Random Rotation
Architecture: Xception Net
Summary:
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 128, 128] 288
BatchNorm2d-2 [-1, 32, 128, 128] 64
LeakyReLU-3 [-1, 32, 128, 128] 0
Conv2d-4 [-1, 64, 128, 128] 18,432
BatchNorm2d-5 [-1, 64, 128, 128] 128
LeakyReLU-6 [-1, 64, 128, 128] 0
Conv2d-7 [-1, 64, 128, 128] 576
Conv2d-8 [-1, 64, 128, 128] 4,096
DepthewiseSeperableConv2d-9 [-1, 64, 128, 128] 0
BatchNorm2d-10 [-1, 64, 128, 128] 128
LeakyReLU-11 [-1, 64, 128, 128] 0
Conv2d-12 [-1, 64, 128, 128] 576
Conv2d-13 [-1, 128, 128, 128] 8,192
DepthewiseSeperableConv2d-14 [-1, 128, 128, 128] 0
BatchNorm2d-15 [-1, 128, 128, 128] 256
MaxPool2d-16 [-1, 128, 64, 64] 0
Conv2d-17 [-1, 128, 64, 64] 8,320
BatchNorm2d-18 [-1, 128, 64, 64] 256
LeakyReLU-19 [-1, 128, 64, 64] 0
Conv2d-20 [-1, 128, 64, 64] 1,152
Conv2d-21 [-1, 128, 64, 64] 16,384
DepthewiseSeperableConv2d-22 [-1, 128, 64, 64] 0
BatchNorm2d-23 [-1, 128, 64, 64] 256
LeakyReLU-24 [-1, 128, 64, 64] 0
Conv2d-25 [-1, 128, 64, 64] 1,152
Conv2d-26 [-1, 256, 64, 64] 32,768
DepthewiseSeperableConv2d-27 [-1, 256, 64, 64] 0
BatchNorm2d-28 [-1, 256, 64, 64] 512
MaxPool2d-29 [-1, 256, 32, 32] 0
Conv2d-30 [-1, 256, 32, 32] 33,024
BatchNorm2d-31 [-1, 256, 32, 32] 512
EntryBlock-32 [-1, 256, 32, 32] 0
LeakyReLU-33 [-1, 256, 32, 32] 0
Conv2d-34 [-1, 256, 32, 32] 2,304
Conv2d-35 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-36 [-1, 256, 32, 32] 0
BatchNorm2d-37 [-1, 256, 32, 32] 512
LeakyReLU-38 [-1, 256, 32, 32] 0
Conv2d-39 [-1, 256, 32, 32] 2,304
Conv2d-40 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-41 [-1, 256, 32, 32] 0
BatchNorm2d-42 [-1, 256, 32, 32] 512
LeakyReLU-43 [-1, 256, 32, 32] 0
Conv2d-44 [-1, 256, 32, 32] 2,304
Conv2d-45 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-46 [-1, 256, 32, 32] 0
BatchNorm2d-47 [-1, 256, 32, 32] 512
MiddleBasicBlock-48 [-1, 256, 32, 32] 0
LeakyReLU-49 [-1, 256, 32, 32] 0
Conv2d-50 [-1, 256, 32, 32] 2,304
Conv2d-51 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-52 [-1, 256, 32, 32] 0
BatchNorm2d-53 [-1, 256, 32, 32] 512
LeakyReLU-54 [-1, 256, 32, 32] 0
Conv2d-55 [-1, 256, 32, 32] 2,304
Conv2d-56 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-57 [-1, 256, 32, 32] 0
BatchNorm2d-58 [-1, 256, 32, 32] 512
LeakyReLU-59 [-1, 256, 32, 32] 0
Conv2d-60 [-1, 256, 32, 32] 2,304
Conv2d-61 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-62 [-1, 256, 32, 32] 0
BatchNorm2d-63 [-1, 256, 32, 32] 512
MiddleBasicBlock-64 [-1, 256, 32, 32] 0
LeakyReLU-65 [-1, 256, 32, 32] 0
Conv2d-66 [-1, 256, 32, 32] 2,304
Conv2d-67 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-68 [-1, 256, 32, 32] 0
BatchNorm2d-69 [-1, 256, 32, 32] 512
LeakyReLU-70 [-1, 256, 32, 32] 0
Conv2d-71 [-1, 256, 32, 32] 2,304
Conv2d-72 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-73 [-1, 256, 32, 32] 0
BatchNorm2d-74 [-1, 256, 32, 32] 512
LeakyReLU-75 [-1, 256, 32, 32] 0
Conv2d-76 [-1, 256, 32, 32] 2,304
Conv2d-77 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-78 [-1, 256, 32, 32] 0
BatchNorm2d-79 [-1, 256, 32, 32] 512
MiddleBasicBlock-80 [-1, 256, 32, 32] 0
LeakyReLU-81 [-1, 256, 32, 32] 0
Conv2d-82 [-1, 256, 32, 32] 2,304
Conv2d-83 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-84 [-1, 256, 32, 32] 0
BatchNorm2d-85 [-1, 256, 32, 32] 512
LeakyReLU-86 [-1, 256, 32, 32] 0
Conv2d-87 [-1, 256, 32, 32] 2,304
Conv2d-88 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-89 [-1, 256, 32, 32] 0
BatchNorm2d-90 [-1, 256, 32, 32] 512
LeakyReLU-91 [-1, 256, 32, 32] 0
Conv2d-92 [-1, 256, 32, 32] 2,304
Conv2d-93 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-94 [-1, 256, 32, 32] 0
BatchNorm2d-95 [-1, 256, 32, 32] 512
MiddleBasicBlock-96 [-1, 256, 32, 32] 0
LeakyReLU-97 [-1, 256, 32, 32] 0
Conv2d-98 [-1, 256, 32, 32] 2,304
Conv2d-99 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-100 [-1, 256, 32, 32] 0
BatchNorm2d-101 [-1, 256, 32, 32] 512
LeakyReLU-102 [-1, 256, 32, 32] 0
Conv2d-103 [-1, 256, 32, 32] 2,304
Conv2d-104 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-105 [-1, 256, 32, 32] 0
BatchNorm2d-106 [-1, 256, 32, 32] 512
LeakyReLU-107 [-1, 256, 32, 32] 0
Conv2d-108 [-1, 256, 32, 32] 2,304
Conv2d-109 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-110 [-1, 256, 32, 32] 0
BatchNorm2d-111 [-1, 256, 32, 32] 512
MiddleBasicBlock-112 [-1, 256, 32, 32] 0
LeakyReLU-113 [-1, 256, 32, 32] 0
Conv2d-114 [-1, 256, 32, 32] 2,304
Conv2d-115 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-116 [-1, 256, 32, 32] 0
BatchNorm2d-117 [-1, 256, 32, 32] 512
LeakyReLU-118 [-1, 256, 32, 32] 0
Conv2d-119 [-1, 256, 32, 32] 2,304
Conv2d-120 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-121 [-1, 256, 32, 32] 0
BatchNorm2d-122 [-1, 256, 32, 32] 512
LeakyReLU-123 [-1, 256, 32, 32] 0
Conv2d-124 [-1, 256, 32, 32] 2,304
Conv2d-125 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-126 [-1, 256, 32, 32] 0
BatchNorm2d-127 [-1, 256, 32, 32] 512
MiddleBasicBlock-128 [-1, 256, 32, 32] 0
MiddleBlock-129 [-1, 256, 32, 32] 0
Conv2d-130 [-1, 512, 16, 16] 131,584
BatchNorm2d-131 [-1, 512, 16, 16] 1,024
LeakyReLU-132 [-1, 256, 32, 32] 0
Conv2d-133 [-1, 256, 32, 32] 2,304
Conv2d-134 [-1, 256, 32, 32] 65,536
DepthewiseSeperableConv2d-135 [-1, 256, 32, 32] 0
BatchNorm2d-136 [-1, 256, 32, 32] 512
LeakyReLU-137 [-1, 256, 32, 32] 0
Conv2d-138 [-1, 256, 32, 32] 2,304
Conv2d-139 [-1, 512, 32, 32] 131,072
DepthewiseSeperableConv2d-140 [-1, 512, 32, 32] 0
BatchNorm2d-141 [-1, 512, 32, 32] 1,024
MaxPool2d-142 [-1, 512, 16, 16] 0
Conv2d-143 [-1, 512, 16, 16] 4,608
Conv2d-144 [-1, 512, 16, 16] 262,144
DepthewiseSeperableConv2d-145 [-1, 512, 16, 16] 0
BatchNorm2d-146 [-1, 512, 16, 16] 1,024
LeakyReLU-147 [-1, 512, 16, 16] 0
Conv2d-148 [-1, 512, 16, 16] 4,608
Conv2d-149 [-1, 1024, 16, 16] 524,288
DepthewiseSeperableConv2d-150 [-1, 1024, 16, 16] 0
BatchNorm2d-151 [-1, 1024, 16, 16] 2,048
LeakyReLU-152 [-1, 1024, 16, 16] 0
AdaptiveAvgPool2d-153 [-1, 1024, 1, 1] 0
Dropout-154 [-1, 1024, 1, 1] 0
ExitBlock-155 [-1, 1024, 1, 1] 0
Linear-156 [-1, 136] 139,400
================================================================
Total params: 2,630,888
Trainable params: 2,630,888
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.06
Forward/backward pass size (MB): 441.02
Params size (MB): 10.04
Estimated Total Size (MB): 451.12
----------------------------------------------------------------
- Objective loss :
MSELoss - Optimizer :
Adam - Learning Rate : 0.0008
- Epochs : 30
