Face detection task

Usage

Download source and data from download page, open ./Face_Detection.ipynb and do task. It's easy.

Mark rules

Maximum mark for this task is 10 points:

• Prepare data (1 points)
  • Student extracted positive and negative samples from data.
• Classifier training (3 points)
  • Student add into model some layers.
  • Student ran fitting and validation accuracy exceeded 90%.
  • Student selected epoch with best validation loss and loaded this epoch weight.
• FCNN model (2 points)
  • Student wrote fcnn model, copy_weight function and visualized activation heat map.
• Detector (1 point)
  • Student wrote get_bboxes_and_decision_function and visualized predicted bboxes
• Precision/recall curve (1 point)
  • Student implements precision/recall curve and plotted it.
• Threshold (1 point)
  • Student find point for recall 0.85
  • Precision/recall graph should stop at recall=0.85
• Detector score (1 point)
  • On test dataset detection score (in graph header) should be 0.85 or greater.

Files

This repository consist of multiple files:

• Face_Detection.ipynb -- main task, read and do.
• get_data.py -- script to download data for task, run automatically from main task. You don't need download data manually.
• scores.py -- scores, which are using in main task.
• graph.py -- graph plotting and image showing functions.
• prepare_data.ipynb -- prepare data to train and test, you may run this script and repeat learning-test procedure to make sure that model haven't over-fitting.

Dataset

Dataset, used in this task is processed FDDB dataset. Processing explained in ./Face_Detection.ipynb and defined in ./prepare_data.ipynb

Authors

• Prepared by Vladimir Lutov: github.com/vslutov, vladimir.lutov@graphics.cs.msu.ru