SVHN digit Predictor

The digit predictor requires Yolo V2 Region of Interest Detector, as downloadable from this great work, based on a ResNet 50. The model for The predictor is CNN Convnet, able to achieve 0.95 accuracy on cropped images.

Usage for python code

0. Requirement

python 3.5
anaconda 4.4.0
tensorflow 1.2.1
keras 2.1.1
opencv 3.3.0
imgaug
Etc.

I recommend to create an anaconda env that is independent of your project. You can create anaconda env for this project by following these simple steps. This process has been verified on MAC OS X10.

$ conda create -n SVHNDetector python=3.5 anaconda=4.4.0
$ source activate SVHNDetector"
(yolo) $ pip install -r requirements.txt

1. Test execution

In this project, 2 distinct model will be used:

a detector for the region of interest, which pretrained weight are called detector.h5
a predictor model on single digit which pretrained weight are called `detector.h5.

Please notice that all the image to be predicted must be in the folder tests\dataset\svhn\imgs; for each call the code will load both models.

2. Downloading and training

This project provides a way to execute following action from the scratch:

Download Training and Test data
Unzip them
Preprocess them in numpy format containing single images cropped
start the training test.

Please notice that is a really time consuming operation, spanning for 5-6 hours. Because of this 2 flags can be setted to bypass operations. the first is controlling downloand and unzipping, the second preprocessing.

traintest(False,False)

Bypass Download and Preprocessing.

3. Jupyter Notebook example

Results are testable with visual plotting, running a jupyter notebok with following command.