This is my classification dogs Lab from my "Artificial Intelligence With Python" Nano Degree.

1. Correctly identify which pet images are of dogs (even if breed is misclassified) and which pet images aren't of dogs.
2. Correctly classify the breed of dog, for the images that are of dogs.
3. Determine which CNN model architecture (ResNet, AlexNet, or VGG), "best" achieve the objectives 1 and 2.
4. Consider the time resources required to best achieve objectives 1 and 2, and determine if an alternative solution would have given a "good enough" result, given the amount of time each of the algorithms takes to run.

1. Install Python 3.6
2. Install Pipenv

1. Clone this project
2. Run pipenv shell
3. Run pipenv install
4. Edit classifier.py or check_images.py

Give execution permission to run_models_batch.sh with this command:

sudo chmod +x run_models_batch.sh

To run that script you need to run './run_models_batch.sh' in your terminal.

This will generate 3 files with the performance detail for each CNN model architectures:

• AlexNet -> alexnet.txt
• ResNet -> resnet.txt
• VGG -> vgg.txt

VGG has the best performance