| | --- |
| | title: Vision Model Interpretability |
| | emoji: 🔍 |
| | colorFrom: red |
| | colorTo: yellow |
| | sdk: gradio |
| | sdk_version: 5.49.1 |
| | app_file: app.py |
| | pinned: false |
| | license: mit |
| | short_description: Interactive Grad-CAM visualization |
| | --- |
| | |
| | # Vision Model Interpretability with Grad-CAM |
| | **David Schechter** |
| |
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| | Upload an image to: |
| |
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| | - classify it using **ResNet-18** |
| | - view the **top-3 predictions** |
| | - visualize a **Grad-CAM heatmap** highlighting the image regions that influenced the model’s decision |
| |
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| | This demo explores **model interpretability in computer vision** by showing how gradients from a convolutional neural network can be used to explain predictions. |
