In this notebook, we will take the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, the code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed.
We break the notebook into separate steps:
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 6: Write our Algorithm
- Step 7: Test our Algorithm
Note:
-
Download the dog dataset. Unzip the folder and place it in the repo, at location
path/to/dogImages. -
Download the human dataset. Unzip the folder and place it in the repo, at location
path/to/lfw. -
Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location
path/to/bottleneck_features.
On the 10 images (mix of humans and dogs) we input manually, we get 8 correct. Below are the predicted results:
