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34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
# Classification of Pets Faces
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Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).
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## Task
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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.
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You need to train a convolutional neural network to classify different breeds of cats and dogs using **Pet Faces** dataset.
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## The Dataset
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We will use the [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), which contains images of 37 different breeds of dogs and cats.
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To download the dataset, use this code snippet:
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```python
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!wget https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz
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!tar xfz images.tar.gz
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!rm images.tar.gz
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```
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**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.
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## Stating Notebook
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Start the lab by opening [PetFaces.ipynb](PetFaces.ipynb)
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## Takeaway
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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.
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