Facial-Landmarks-Detection-Pytorch
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braindotai
Implementation of facial landmarks detection using pytorch on iBUG 300W dataset. Why Xception Net? Because it provides satisfactory accuracy, with least computational requirement and memory utilizatio...
Face Landmarks Detection
Data preprocessing
-
Augmentations for faces:
- Random Brightness
- Random Contrast
- Random Gamma
- Random Saturation
- Random Hue
- Random Rotation
-
Augmentations for landmarks:
- Random Rotation
Model
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
----------------------------------------------------------------
Training hyperparameters
- Objective loss :
MSELoss - Optimizer :
Adam - Learning Rate : 0.0008
- Epochs : 30
Training progress
Final Results
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Author - Rishik Mourya
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Implementation of facial landmarks detection using pytorch on iBUG 300W dataset. Why Xception Net? Because it provides satisfactory accuracy, with least computational requirement and memory utilizatio...