Face-Attribute-Classification

Eyeglasses, Beard, Mustache, and Hat classification using Tensorflow.

Problem Analysis

The face attribute classification is a multi label classification with four classes for each image including eyeglasses, mustache, beard, and hat. Each class on each target has a binary of 0 or 1 where 0 represents the absence of the class and 1 indicates the existence of the class. For example, if we have a target with values of 1, 0, 0, 1 it means that this face image has the attributes of eyeglasses and hat.

Transfer Learning

Transfer learning is a pretty good technique to speed up the training process and avoid overfitting due to lack of training data. There are several models such as VGG16, InvceptionV3, Resnet, etc., which have been trained using the imagenet dataset. We can use the pre-trained weights and then freeze some or all of the convolution layers from the model. Lastly, add some Dense layers for the classification head with the Sigmoid activation for the last Dense.

Sigmoid Activation Rather Than Softmax

The idea behind the softmax function is to normalize the data such that the values of the output layer in the network lie in the range of 0 to 1 and the sum of total values equal to 1. These values can then be interpreted as probabilities, where the highest probability is most likely the best candidate label for the sample in the dataset. Of course, this is acceptable for single label data because each label is considered mutually exclusive. For multi-label data another option should be considered. Because we cannot use softmax in this case, we should use some other functions that have a range of 0 or 1, so that these can be interpreted as probabilities. The sigmoid function is a good use for this task. Since the predictions in the output layer of the network are independent of the other output nodes, we can set a threshold to determine the classes for which the sample belongs. In our case, the threshold for the output layer is 0.5.

Getting Started

Data Preparation

First you need to download the CelebA dataset here

Convert CelebA annotation

Convert CelebA annotations using the following command

python src/convert_celeba_annotation.py 
  --annotation annotation.txt 
  --json selected_celeba_annotation.json

Now you have the selected_celeba_annotation.json consisting of annotation for the selected classes, in this case we only select the eyeglasses, beard, mustache, dan hat class. In order to make the model can learn the negative class, there is another flag called mixed_num that represents the number of annotation for non-selected class to be added.

Create tfrecord files

Create tfrecord files for training, validation, and testing with ratio : 80% training, 10% validation, and 10% testing.

python src/create_tfrecords.py 
  --json selected_celeba_annotation.json
  --dataset dataset

Training

python src/train.py
  --model resnet50
  --model_dir models
  --train_tfrecord train.tfrecord
  --val_tfrecord val.tfrecord

By default, this command will start the training for 40 epochs with 1e-4 of learning rate. The model will be trained with transfer learning using the imagenet weights provided by Tensorflow and we will freeze the weights so that only classification head will be trained.

Predict Single Image

python predict.py
  --model_path resnet50_2020_05_14_10_45.h5
  --image image.jpg

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