e095e89862
- Replace broken Microsoft Azure URL with Oxford-IIIT Pet Dataset URL - Update download command to use https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz - Update extraction commands to handle images.tar.gz instead of petfaces.tar.gz - Add code to organize images by breed (Oxford dataset uses filename-based organization) - Update documentation to explain the dataset structure - Applied changes across all 49 language translations Co-authored-by: leestott <2511341+leestott@users.noreply.github.com> |
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| PetFaces.ipynb | ||
| README.md | ||
README.md
Classification of Pets Faces
Lab Assignment from AI for Beginners Curriculum.
Task
Imagine you need to develop and application for pet nursery to catalog all pets. One of the great features of such an application would be automatically discovering the breed from a photograph. This can be successfully done using neural networks.
You need to train a convolutional neural network to classify different breeds of cats and dogs using Pet Faces dataset.
The Dataset
We will use the Oxford-IIIT Pet Dataset, which contains images of 37 different breeds of dogs and cats.
To download the dataset, use this code snippet:
!wget https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz
!tar xfz images.tar.gz
!rm images.tar.gz
Note: The Oxford-IIIT Pet Dataset images are organized by filename (e.g., Abyssinian_1.jpg, Bengal_2.jpg). The notebook includes code to organize these images into breed-specific subdirectories for easier classification.
Stating Notebook
Start the lab by opening PetFaces.ipynb
Takeaway
You have solved a relatively complex problem of image classification from scratch! There were quite a lot of classes, and you were still able to get reasonable accuracy! It also makes sense to measure top-k accuracy, because it is easy to confuse some of the classes which are not clearly different even to human beings.