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| title: Era Week12 Lightning.resnet | |
| emoji: 🐠 | |
| colorFrom: green | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 3.39.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # CIFAR10 Image classification using a Custom ResNet Model | |
| ## What is the app about? | |
| [This app](https://huggingface.co/spaces/nviraj/ERA-V1-Assignment12) built using [Gradio](https://www.gradio.app/) provides an interface to run inferences for CIFAR10 image classification using a custom ResNet model trained using PyTorch and Lightning with \>90% accuracy. | |
| ### What input does it require? | |
| - **Example Input** | |
| - Please note that example inputs have been provided for you to test the app below the Submit button. Please select one of the examples to see the app in action | |
| - **Inference Related** | |
| - Image | |
| - Any image for the following 10 CIFAR10 classes [airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks] | |
| - It accepts any resolution and image type | |
| - How many top classes to predict? | |
| - Max of 10 classes | |
| - Do you want to show the GradCAM image? | |
| - This shows you features deemed important by the model in making the prediction | |
| - Which layer of the model do you want to generate GradCAM for? | |
| - A network has multiple layers and it is sequentially shown as a drop down. Every layer incrementally identifies bigger parts of the image. Have fun generating the visualization for different layers. | |
| - By what factor do you want to overlay the original image on GradCAM? | |
| - Smaller the factor more prominent is GradCAM Hotspot. | |
| - As you increase the factor the original image becomes more opaque and prominent over the heatmap | |
| - **Diagnostics Related** | |
| - Do you want to show Misclassified Images and how many? | |
| - This comes in handy to see where the model fails to predict accurate classes | |
| - Do you want to see GradCAM for Misclassified Images and how many? | |
| - This is useful to see what parts of the image led to incorrect classification | |
| ### What is the output? | |
| - Predictions for top number of classes chosen as well as the predicted class | |
| - Either the original image or image + GradCAM heatmap based on input chosen | |
| - Misclassified Images by the model | |
| - GradCAM for Misclassified Images by the model | |
| ### How was the model built? | |
| - Model was trained using a custom ResNet model trained for just 24 epochs with 91.4% validation accuracy | |
| - The code can be found here | |
| - [Notebook](https://github.com/nviraj/era-v1/blob/main/Session%2012/Submission/ERA%20V1%20-%20Viraj%20-%20Assignment%2012.ipynb) | |
| - [Modules](https://github.com/nviraj/era-v1/tree/main/Session%2012/Submission/modules) | |
| - [Model](https://github.com/nviraj/era-v1/tree/main/Session%2012/Submission/models) | |
| ### Links | |
| - [GradCAM?](https://arxiv.org/abs/1610.02391) | |
| - [Pytorch](https://pytorch.org/) | |
| - [Pytorch Lightning](https://www.pytorchlightning.ai/index.html) | |
| - [ResNet](https://arxiv.org/pdf/1512.03385.pdf) | |