| library_name: fastai | |
| tags: | |
| - image-classification | |
| - fastai | |
| - computer-vision | |
| # flower-classifier | |
| This is an image classification model trained using fastai. It classifies images into the following categories: | |
| `['golden_dewdrop', 'peepal_tree']` | |
| ## Model Details | |
| - **Architecture**: `<function resnet34 at 0x7a5c724aa2a0>` | |
| - **Image Size**: `224` | |
| - **Training Data**: Custom dataset (flower images) | |
| ## How to use this model | |
| ```python | |
| from fastai.vision.all import * | |
| from huggingface_hub import hf_hub_download | |
| # Download the model file | |
| model_path = hf_hub_download(repo_id='lucypatrice/flower-classifier', filename='model.pkl') | |
| # Load the fastai learner | |
| learn = load_learner(model_path) | |
| # Example prediction | |
| img = PILImage.create('your_image.jpg') # Replace with your image path | |
| pred, pred_idx, probs = learn.predict(img) | |
| print(f"Prediction: {pred} (confidence {probs[pred_idx]:.2%})") | |
| ``` | |
| ## Training Metrics | |
| (You can add more detailed metrics, confusion matrices, etc. here after evaluating.) | |