Age and Gender Prediction with UTK Dataset

Project Overview

The goal of this project is to build an age and gender classification model trained on the UTKFace dataset.

Key Highlights

• MobileNetV3.Small: Instead of using a large, complex model, we've chosen to use a very small model with (relatively) simple architecture and to and make it competitive with significantly larger and computationally intensive models like VGG or ResNet mainly by focusing on optimizing the preprocessing pipeline and the loss function.

• Training Process: Extensive hyperparameter tuning and experiment tracking using Weights & Biases to determine the most robust configuration

• Iterative Approach: We've used an iterative approach by first building a model that was tuned to maximize overall gender and age prediction accuracy. After identifying the subsets of the dataset where the model performed poorly (underrepresented age groups, specific combinations of gender and skin color etc.) we've used these insights to build augmentation based oversampling strategy that significantly improved performance for underrepresented groups with minimal impact on accuracy for the rest of the sample.

• Evaluation: Comprehensive evaluation of results was performed both across sub-samples (based on age groups, skin color and other attributes) and on an individual basis using LIME.

Performance Overview

Our best-performing models achieved the following results:

Model	notes	Gender Acc.	Age MAE
MobileNetV3.Small	no augmentations	0.921	5.335
MobileNetV3.Small	basic augmentations2	0.925	5.105
MobileNetV3.Small	aug. based oversampling3	0.939	4.731
MobileNetV3.Small	no weights* + aug. oversampling	0.926	5.148

2. v1-Base-Model 3. v2-Improved-Model * trained from scratch, others MobileNetV3 small models are using IMAGENET1K_V1 weights

We've been able to achieve very high performance to some selected baseline models (trained and evaluated with the UTK dataset)*:

Model	notes	Gender Acc.	Age MAE	*
XGBoost	feat. extraction	93.80	5.89
SVC	feat. extraction1	94.64	5.49
MobileNetV3.Small		0.939	4.694
VGG_f	pre-trained on VGGFace	0.934	4.86	-
ResNet50_f	(VGGFace2)	0.944	4.65
SENet50_f	(VGGFace2)	0.949	4.58

*Sheoran, V., et al. (2021). Age and Gender Prediction using Deep CNNs and Transfer Learning. arXiv. https://arxiv.org/pdf/2110.12633

Performance Summary:

Gender Prediction:

Age Prediction:

Key findings:

• The model showed consistent performance across most age groups, with some challenges in very young (0-4 years) and very old age ranges.
• Performance was relatively stable across different subject luminance levels (heuristic for skin color), indicating low bias related to skin color.
• Image quality had a noticeable impact on prediction accuracy, with lower quality images generally resulting in reduced performance.
• LIME analysis revealed the facial features most influential in the model's decisions, providing insights into its reasoning process.

Structure / Table of Contents:

• 1.0_Data_Analysis (with code)

  • Analyzing various image aspects like color variance, luminance, quality etc. using various approaches like BRISQUE, FFT, Laplacian variance etc. to determine sub samples/bins that could be used for evaluation.

• 2.0_Preprocessing and Hyperparameter Tuning (with code:

  • Overall model structure and component/parameter selection
  • Preprocessing: transformer selection, augmentation and balancing.

• 3.0_Performance_Analysis_and_Improvements (with code)

  • Evaluation of the initial model
  • Error analysis
  • Building an improved v2 model
  • Comparing final and initial model

(note: please use these links to view the actual notebooks because interactive charts/views will not be visible if opened directly in GitHub)

Source files:

(most important ones to see:)

• src/models/MobileNet/classifier.py

  • Pytorch Lighting Module

• src/models/MobileNet/data_defs.py

  • Dataset/Datmodule implementation

• src/models/MobileNet/metrics.py

  • Main metrics/evaluation functions

• Individual experiment and Sweep configurations

  • (might be clearer check the wandb report links for the "production" below to results tables)

Dataset Summary

The model was trained and fine-tuned using the UTKFace dataset, which contains over 20,000 face images with annotations for age, gender.

Dataset split:

• 80% (18,956 images) training set
  • hyperparameter tuning was done using a 20% validation subsample
• 20% (4,740 images) test set

The training set was further split into 80% training and 20% validation during hyperparameter tuning. The final models were trained on the full training set.

Key characteristics:

• Age range: 0 to 116 years
• Gender: Binary classification (male/female)
• Relatively diverse ethnic representation
• Varying image quality and lighting conditions

Model Architecture

• Base model: MobileNetV3-Small
• Pretrained weights: IMAGENET1K_V1
• Custom classifier layers:
  • Global Average Pooling
  • Two heads with dropout and L1 regularization:
    1. Binary gender classifier
    2. Age regressor
• Optimizer: AdamW
• Learning rate scheduler: OneCycle with Cosine Annealing
• Regularization: L1 regularization and dropout

Training Pipeline

1. Dynamic data augmentation
2. Weighted augmentation-based oversampling
   • Generated additional samples for underrepresented age groups
   • Used intensive transformations to increase data diversity
3. Transfer learning with ImageNet weights
4. One-cycle learning rate scheduling with cosine annealing
   • Achieved faster convergence (15-20 epochs) compared to other schedulers
5. Mixed precision training
6. Batch size: 256
7. Number of epochs: 15-20