/FlowerClassification

This project aims to train a deep learning model using the flower-photos dataset, which consists of 5 classes of flowers: sunflowers, tulips, dandelions, roses, and daisies.

Primary LanguageJupyter Notebook

Project Descriptions

The flower_photos dataset is a popular image classification dataset consisting of 5 classes of flowers: sunflowers, tulips, dandelions, roses, and daisies. Each class contains a certain number of images, with the following distribution:

* Sunflowers: 699 images
* Tulips: 799 images
* Dandelions: 898 images
* Roses: 641 images
* Daisies: 633 images

This dataset is commonly used for deep learning projects and machine learning tutorials as a beginner-friendly dataset for image classification tasks. The goal of our project is to train a deep learning model using this dataset for learning purposes. By working with this dataset, we aim to gain hands-on experience in building and training deep learning models, understanding image data preprocessing, and interpreting model performance. This project will provide valuable insights into the practical implementation of deep learning algorithms using real-world image data, and help us improve our understanding of machine learning concepts and techniques.