文件列表:

Image Classifier Project.html
predict.py
train.py

# Create-Your-Own-Image-Classifier Udacity X AWS AI with Python Programming Project II # Image Classification with VGG16 This project implements image classification using the VGG16 model. It includes a training script to train the model and a prediction script to make predictions on new images. ## Table of Contents - [Overview](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#overview) - [Requirements](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#requirements) - [Usage](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#usage) - [Training](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#training) - [Prediction](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#prediction) - [Project Structure](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#project-structure) - [License](https://github.com/dolivon123456/Create-Your-Own-Image-Classifier/blob/master/#license) ## Overview This project uses the VGG16 model for image classification. It includes training and prediction scripts written in Python using PyTorch. The training script allows you to train the model on your dataset, while the prediction script allows you to make predictions on new images using a pre-trained model checkpoint. ## Requirements Before running the scripts, make sure you have the following requirements installed: - Python 3.x - PyTorch - torchvision - Pillow ## Steps Install the required libraries using the following command: ```bash pip install torch torchvision pillow Usage Training To train the model on your dataset, follow these steps: Organize your dataset into train and valid folders inside a root data directory. Run the training script: python train.py data_directory --arch vgg16 --hidden_units 512 --learning_rate 0.001 --epochs 3 --save_dir checkpoints --gpu Replace data_directory with the path to your root data directory. Prediction To make predictions on new images using a pre-trained VGG16 model, follow these steps: Prepare an image for prediction. Run the prediction script: python predict.py input_image checkpoint.pth --category_names cat_to_name.json --gpu Replace input_image with the path to the image file, checkpoint.pth with the path to your pre-trained model checkpoint, and optionally provide --category_names if you have a JSON file mapping class indices to human-readable class names. Project Structure - data/ # Sample data for testing - checkpoints/ # Directory to save model checkpoints - predict.py # Prediction script - train.py # Training script - README.md # Project documentation License This project is licensed under the MIT License - see the LICENSE file for detail

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