maxencefaldor/dog-classification

A dog classifier that predicts the breed provided an image.

Dog classifier

The goal of this project is to create a dog classifier using state-of-the-art models for classification and localization. The pipeline processes real-world, user-supplied images. First, it tries to detect human faces. If supplied an image of a human face, the code will identify the resembling dog breed. Else, the algorithm will identify an estimate of the canine’s breed using a CNN.

Detect Humans

I used OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files. I downloaded one of those and stored it in the haarcascades directory.

Detect Dogs

I downloaded VGG-16 pre-trained model to detect dogs in images. I also explored other pre-trained networks such as Inception-v3 and ResNet-50. In order to check if an image contains a dog with the VGG-16 model, we need to check if the pre-trained model predicts an index between 151 and 268 (categories from 'Chihuahua' to 'Mexican hairless').

Create a CNN to Classify Dog Breeds (from scratch)

I created a CNN that classifies dog breeds from scratch. The neural network has three convolutional layers with max pooling and ReLU activation. I used data augmentation with random horizontal flip and random rotation.

The model achieves 15% accuracy for the classification task of 133 different dog breeds.

Create a CNN to Classify Dog Breeds (using transfer learning)

I used transfer learning to improve the model's performance. The dataset is small and similar to ImageNet (there are a lot of image of dogs in ImageNet) therefore I chose to get the convolutional layers from VGG16 fixed and train my own classifier on top.

The model achieves 72% accuracy for the classification task of 133 different dog breeds.

Data

• Download the dog dataset
• Download the human dataset