This project uses a dataset of flower images for classification. The dataset includes multiple categories of flowers, with varying numbers of images per category. Here's a sample of the categories included:
| Category | Number of Images |
|---|---|
| Alpine sea holly | 43 |
| Buttercup | 71 |
| Fire lily | 40 |
##Project Description This project aims to classify different types of flowers using machine learning techniques. I utilized a pre-trained ResNet50 model and fine-tuned it on flower dataset to achieve accurate classification results.
- Python 3.x
- PyTorch
- torchvision
- PIL
- Prepare the flower dataset in the required format (https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)
- Adjust the data loading and preprocessing steps as needed
- Run the training script to fine-tune the ResNet50 model
- Evaluate the model's performance on a test set
- Use the trained model for predictions on new flower images