ComputerVision PeerReviewed Project: Facial Keypoints Detection

Project 1: Facial Keypoints Detection

Project Experience

This is the first computer vision project which allows me to apply deep learning computer vision architectures(using Pytorch) to build a facial keypoint detection system for

• tracking
• pose recognition
• filters
• emotion recognition

Image Augmentation

Introduce generalization(randomness) to detect and learn structures.

Transform Steps

Only the following transformation techniques were chosen to avoid unnecessary impacts on keypoints.

• Rescale to 250 for width and height
• RandomCrop to 224
• Normalize & Convert ToTensor

Architecture & Performance

BatchSize, Epochs, Loss & Optimization Functions(only using CPU)

• BatchSize : 10 (notebook stopped working if used batchsize higher than 20)
• Epochs : 8 (lower epochs was chosen due to hardware capacity constraint)
• Loss : SmoothL1Loss (significant loss reduction since earlier epochs)
• Optimizer : Adam

A simple model with 4 Conv2D layers, ReLU Activation, MaxPooling+BatchNorm in every layer, and Dropout to prevent overfitting.

Worklist

For project instructions, please refer to https://github.com/udacity/P1_Facial_Keypoints

The project is broken up into four Python notebooks; only Notebook 2, 3 and models.py file are graded.

Files in Order

• data_load.py; models.py;
• Notebook 1. Load and Visualize Data.ipynb
• Notebook 2. Define the Network Architecture.ipynb
• Notebook 3. Facial Keypoint Detection, Complete Pipeline.ipynb
• Notebook 4. Fun with Keypoints.ipynb

Additional Helpful Resources

• https://arxiv.org/pdf/1603.07285.pdf
• http://cs231n.stanford.edu/slides/2016/winter1516_lecture11.pdf
• http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf
• https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0

Packages

Python, Pytorch

LICENSE: This project is licensed under the terms of the MIT license.