Dog-Breed Classifier

Submission Files

The submission includes all required, complete notebook files.

Question 1: Assess the Human Face Detector

The submission returns the percentage of the first 100 images in the dog and human face datasets that include a detected, human face.

Implement a Dog Detector

Use a pre-trained VGG16 Net to find the predicted class for a given image. Use this to complete a dog_detector function below that returns True if a dog is detected in an image (and False if not).

Question 2: Assess the Dog Detector

The submission returns the percentage of the first 100 images in the dog and human face datasets that include a detected dog.

Specify DataLoaders for the Dog Dataset

Write three separate data loaders for the training, validation, and test datasets of dog images. These images should be pre-processed to be of the correct size.

Question 3: Describe your chosen procedure for preprocessing the data.

Answer describes how the images were pre-processed and/or augmented.

Model Architecture

The submission specifies a CNN architecture.

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.

Answer describes the reasoning behind the selection of layer types.

Train the Model

Choose appropriate loss and optimization functions for this classification task. Train the model for a number of epochs and save the "best" result.

Test the Model

The trained model attains at least 10% accuracy on the test set.

Model Architecture

The submission specifies a model architecture that uses part of a pre-trained model.

Question 5: Model Architecture

The submission details why the chosen architecture is suitable for this classification task.

Train and Validate the Model

Train your model for a number of epochs and save the result wth the lowest validation loss.

Test the Model

Accuracy on the test set is 60% or greater.

Predict Dog Breed with the Model

The submission includes a function that takes a file path to an image as input and returns the dog breed that is predicted by the CNN.

Write your Algorithm

The submission uses the CNN from the previous step to detect dog breed. The submission has different output for each detected image type (dog, human, other) and provides either predicted actual (or resembling) dog breed.

Test Your Algorithm on Sample Images!

The submission tests at least 6 images, including at least two human and two dog images.

Question 6: Weaknesses and Improvements

Submission provides at least three possible points of improvement for the classification algorithm.