This project aims to classify flower species using pretrained deep learning models (EfficientNet, ResNet, and VGG). The classifier can identify the species of a flower from an image and provides a detailed classification.

• Image classification using pretrained models: EfficientNet, ResNet50, and VGG16.
• Supports image input in local file format.
• Implements early stopping to prevent overfitting.

• Python 3.8
• Anaconda or Miniconda for environment management

1. Clone the repository:

   git clone https://github.com/yourusername/flower-species-classification.git
   cd flower-species-classification

2. Create and activate a conda environment:

   conda create --name flower-classifier python=3.8
   conda activate flower-classifier

3. Install the required packages:

   conda install pytorch torchvision numpy tqdm matplotlib

1. Prepare the data:

   Ensure your data is organized in the following structure:

   flowers/
       train/
       valid/
       test/
   

2. Train the classifier:

   python train.py <data-folder-path> --arch <model-name> --epochs <int> --learning_rate <float> --gpu

3. Evaluate the classifier:

   Once the training is complete, the model will be saved as a checkpoint in the specified directory.

   python predict.py <image-path> <checkpoint-path> --topk <int> --category_names <json-file-path> --gpu

This project was created as part of the AWS AI ML Nanodegree program.