In this project, we use Tensoflow (Keras) to implement and train machine learning algorithms to automatically detect dog breeds in pictures.

We implement a CNN (Convulotionnal Neural Network) from scratch, and fine-tune pre-trained models thanks to transfer learning. We use KerasTuner for the hyperparameters search and Tensorboard to track the results.

We develop a wrapper for Keras, KerasTuner & TensorHub in order to :

• Automatically generate neural networks and their structure
• Train neural networks by applying hyperparameters search strategies
• Visualize the performances of the generated models

We develop a MVP with Streamlit for users to esaily classify theirs images to obtain a breed prediction using the drag & drop feature.

• P6_01_test_notebook.ipynb: machine learning experiments & code for the wrapper
• P6_02_app_README.md: README of the MVP and link to the github repo
• P6_03_app.zip: source code of the MVP
• P6_04_presentation.pdf: projects presentation slideshow (french)

The training data used in this notebook is the Stanford Dogs Dataset.