In this project, a dog breed classifier is built using transfer learning on VGG16 model, which can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, the classifier will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
- Python versions 3.6
- Library and packages: pytorch, cv2, tqdm, PIL, os, glob, numpy, pandas, matplotlib, pickle
dhaarcascades: contains the face detector files
images: contains the images used in the notebooks or test sample images
dog_app.ipynb: the main notebook contains all the codes
report.html: html format of dog_app.ipynb
workspace_utils.py: functions used in the notebooks
The dog classifier built on top og VGG16 results in 87% accuracy score on the test data. Some sample results are given below.
Acknowledge to Udacity for providing the starter codes.