Update README.md

README.md

@@ -12,37 +12,30 @@ license: mit
 
 # CustomResNet with GradCAM - Interactive Interface
     
-### Implimented a simple Gradio interface to infer on CustomResNet model and get GradCAM results
+This project Impliments a simple Gradio interface to perform inference on CustomResNet model and generate the GradCAM visualization results.
 
 ## Task : 
 
-Classification on CIFAR10 dataset using Custom ResNet model by using pytorch lightning. 
+The task involves performing classification on the CIFAR-10 dataset using the Custom ResNet model built with PyTorch and PyTorch Lightning. 
 
 ## Files :
 
--> requirements.txt file contains necessary packages to install.
+1. `requirements.txt`: Contains the necessary packages required for installation.
+2. `custom_resnet.py`: Contains the CustomResNet model architecture.
+3. `CustomResNet.pth`: Trained model checkpoint file containing model weights.
+4. `examples/`: Folder containing example images (e.g., cat.jpg, car.jpg, etc.).
+5. `app.py`: Contains the Gradio code for the interactive interface. Users can select input images or examples and view GradCAM images, predictions, and top-k classes.
+6. `misclassified_images/`: Folder containing misclassified images.
 
--> custom_resnet.py file contains model architecture.
+## Implementation
 
--> CustomResNet.pth contains trained model checkpoints (weights).
+The following features are implemented using Gradio:
 
--> examples folder : 10 example images like cat.jpg, car.jpg,..
+1. **GradCAM Images:** Users are prompted to choose whether they want to view GradCAM images. They can specify the number of images, the target layer, and adjust opacity.
+2. **Misclassified Images:** Users have the option to view misclassified images and apply GradCAM visualization to them.
+3. **Upload and Select Images:** Users can upload new images or select from a set of 10 example images.
+4. **Top Classes:** Users can choose how many top classes they want to see in the prediction results.
 
---> app.py contains gradio code. By using gradio here implemented by selecting input images or examples output display the gradcam image and prediction and top k classes. 
-
---> misclassified_images folder :  10 misclassified images 
-
-## Implimentation : 
-
-First loaded the model by using model weights .pth file.
-
-### By using GRADIO we created these features :
-
--> Asking the user they want to see GradCAM images if yes then how many images, from which layer and also allow opacity change.
-
--> Providing the option to user they want to view misclassified images, and how many images. If they want to apply grad cam for misclassified images.
-
---> Option to upload new images, as well as select from 10 example images.
-
---> Providing one more option how many top classes they want to see.
+## Usage
 
+1. Run the `app.py` script to launch the interactive Gradio interface.