32 lines
1.3 KiB
Markdown
32 lines
1.3 KiB
Markdown
# Classification of Pets Faces
|
|
|
|
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).
|
|
|
|
## 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 **Pet Faces** dataset, derived from [Oxford-IIIT](https://www.robots.ox.ac.uk/~vgg/data/pets/) pets dataset. It contains 35 different breeds of dogs and cats.
|
|
|
|
|
|
|
|
To download the dataset, use this code snippet:
|
|
|
|
```python
|
|
!wget https://mslearntensorflowlp.blob.core.windows.net/data/petfaces.tar.gz
|
|
!tar xfz petfaces.tar.gz
|
|
!rm petfaces.tar.gz
|
|
```
|
|
|
|
## Stating Notebook
|
|
|
|
Start the lab by opening [PetFaces.ipynb](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.
|