This notebook builds an end-to-end multi-class image classifier using TensorFlow 2.x and TensorFlow Hub.
1. Problem
Identifying the breed of a dog given an image of a dog.
When I'm sitting at the cafe and I take a photo of a dog, I want to know what breed of dog it is.
2. Data
The data we're using is from Kaggle's dog breed identification competition.
https://www.kaggle.com/c/dog-breed-identification/data
3. Evaluation The evaluation is a file with prediction probabilities for each dog breed of each test image.
https://www.kaggle.com/c/dog-breed-identification/overview/evaluation
4. Features Some information about the data:
We're dealing with images (unstructured data) so it's probably best we use deep learning/transfer learning. There are 120 breeds of dogs (this means there are 120 different classes). The list of breeds is as follows:
affenpinscher, afghan_hound, african_hunting_dog, airedale, american_staffordshire_terrier, appenzeller, australian_terrier, basenji, basset, beagle, and many more.
There are around 10,000+ images in the training set (these images have labels). There are around 10,000+ images in the test set (these images have no labels, because we'll want to predict them).
