| title: "AML 16" | |
| version: "1.0.0" | |
| emoji: "🤗" | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: "5.29.0" | |
| app_file: app.py | |
| pinned: false | |
| # AML 16 | |
| This is a demo application for the best-performing model (Swin-Large) created for the AML 16 project. | |
| The app uses Gradio to provide an interactive interface where users can upload an image, view the top-1 predicted scene category, see a reference image from the predicted class, and explore the top-5 prediction probabilities in a bar chart. | |
| The model was trained for scene classification and deployed using Hugging Face Spaces. | |
| - predict.py | |
| This file handles loading the trained Swin-Large model and making predictions. | |
| It loads the model weights from Hugging Face Hub, applies the correct image preprocessing, and outputs: | |
| The uploaded image, | |
| A reference image from the predicted class, | |
| The Top-5 prediction probabilities. | |
| The model was customized with an updated classifier head, and class labels are loaded from a labels.json file. A random sample image from the predicted class folder is also shown for better visualization. | |
| - app.py | |
| This file builds the Gradio interface. | |
| It lets users upload an image, runs the prediction using predict.py, and displays: | |
| The uploaded image, | |
| An image for the top-1 predicted class, | |
| The predicted class label, | |
| A bar chart showing the Top-5 prediction probabilities. |