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| title: CIFAR10 classification with ResNet | |
| emoji: 🔥 | |
| colorFrom: green | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 3.39.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| ### Gradio UI for CIFAR10 classification with ResNet | |
| ## How to use? | |
| 1. Select if you want visualize the misclassified images & Select the count of misclassified images. | |
| 2. Select if you want to visualize the GradCAM images & Also select count of Gradcam images, Model layer and Opacity of the resulting image. | |
| 3. Click on the upload button to upload the local image to be used for prediction and select the image for prediction. | |
| 4. If you want use one of the sample images, please pick one from the list of 10 sample images. | |
| 5. Select the top n classes for which you want see the model performance. | |
| 6. Click on the Run button | |
| 7. On the right side of the interface, the top view displays the selected number of misclassified images. | |
| 8. The second view displays the GradCAM output. | |
| 9. And Final view displays the top n predicitons for the given image. | |
| ## Components Used: | |
| 1. `gr.Dropdown` : Used for selecting the number of images for Misclassified & GradCAM output and also for the top n classes to be displayed. | |
| 2. `gr.Checkbox` : Used for boolean inputs like if user wants to visualize Misclassified or if they want to visualize gradCAM images. | |
| 3. `gr.Slider` : Used to select the opacity paramter to be used with GradCAM viaualization. | |
| 4. `gr.Gallery`: Used to display a numebr of images, used for displaying input images and output images. | |
| 5. `gr.UploadButton`: A generic file uplaod button, used for picking and uploading local image file for prediction. | |
| 6. `gr.Button`: Used for calling the main prediction module. | |
| 7. `gr.Label`: Used for displaying the top n classification results. | |
| ## HuggingFace Interface | |
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