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In this project, the goal is to train a model for segmenting various parts of face.

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tshr-d-dragon/Face_Segmentation

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Face_Segmentation

Analysis of the problem statement and Dataset:

In this project, the goal is to train a model for segmenting various parts of face.

Dataset: link

Number of Images: Train(19535) and Validation (2653)

Original Dataset have 18 classes can be referred from here, However, I compounded classes like left_ear and right_ear to ear, etc. and also neglected the unnecessary classes like hair, hat, etc.

Classes: 8

  • 0: Background
  • 1: Face Skin
  • 2: Eyebrows
  • 4: Eyes
  • 6: Nose
  • 7: Mouth
  • 13: Ears
  • 16: Glasses

Evaluation Metric: Precision, Recall, Dice Score (F1-Score), Jacard Score (mIoU Score)

Loss: Dice Loss + Categorical_Cross_Entropy Loss + Jacard Loss

Images are in very big in size with varying orientation (Portrait as well as Landscape). So, for this project, I have decided to used images and masks of resized value of 256 x 256.

Pixel wise distribution of classes per image:

Distribution of Dataset

Model Training:

  • I used PSPNet with ResNet50 as a backbone and Deeplabv3+ model with EfficientNetB0 as a backbone
  • I used 4000 training images and 800 validation images only for training because of shortage of infrastructure
  • I trained the model for 39 epochs with initial learning rate of 0.1 with SGD (Stochastic Gradient Descent) optimizer and Reduce Learning Rate on Plateau with patience of 4 and factor of 0.5)
  • I also used Early Stopping callback with patience of 10 epochs to prevent overfitting

Graphs (Loss, DiceScore (F1-Score), IOU_Score):

  • PSPNet
Learning Rate loss F1 IOU
LR loss F1 IOU
  • Deeplab
Learning Rate loss F1 IOU
LR loss F1 IOU

Predictions on 3 images from Test Dataset:

  • PSPNet
Image Ground Truth Prediction
1 2 3
1 2 3
1 2 3
  • Deeplab
Image Ground Truth Prediction
1 2 3
1 2 3
1 2 3

Performance Comparison:

  • On Complete (Train: 4000 and Val: 800 images) Dataset:

TrainVal

  • On Test Dataset (7766 images):

Test

Future Steps:

  • Try training model with complete dataset
  • Try training model with different loss functions
  • Try training Deeplabv3+ and PSPNet models with different backbones
  • Try with different model such as Unet, LinkNet, ICNet with different backbones
  • Try training model further for more epochs
  • Try gathering more data for training by new annotations and augmentations
  • Try training the model further with smaller learning rate with Adam or SGD (with momentum)

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In this project, the goal is to train a model for segmenting various parts of face.

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