Update README.md

README.md

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 pinned: false
 license: mit
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+# Pytorch LIghtning Model Trained for CIFAR10
+
+- This application showcases the inference capabilities of a model trained on the CIFAR dataset.
+-  The architecture utilized is based on Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun's work on Deep Residual Learning for Image Recognition (arXiv:1512.03385).
+- It has been recreated and trained on the CIFAR10 dataset, achieving an accuracy of 85%+ within 24 epochs. The training process was accelerated using the One Cycle Policy technique. The model implementation is done using PyTorch Lightning.
+- The model is coded using PyTorch Lightning 2.2.2. 
+
+Mentioned below is the link for Training Repository [Training Repo Link](https://github.com/Shivdutta/ERA2-Session13)
+
+- Following the training process, the model is saved locally and then uploaded to Gradio Spaces.
+- Attached below is the link to [download model file](https://huggingface.co/spaces/Shivdutta/S13/blob/main/model.pth)
+
+-  This app has two features :
+
+  - **GradCam:** 
+"   - To visualize the specific regions of the image that the model focuses on during inference
+    - This insight can guide the development of augmentation strategies aimed at enhancing model accuracy"
+  - **Misclassified Image:**
+    - Despite achieving a test accuracy of over 85% during model training, there remained a 12% misclassification rate for certain images.
+    - This feature facilitates the visualization of misclassified images alongside their correct and incorrect labels.
+    - Such visualization aids in devising strategies to enhance accuracy, especially for specific classes."
+
+## Usage:
+
+### GradCam
+
+- Upload an image or choose from predefined examples.
+- Adjust the opacity.
+- Specify the number of top classes you wish to view.
+- Click the 'Submit' button to generate the results."
+
+### Misclassified Images
+
+- Select the number of misclassified images you want to see
+- Click on `Display Misclassified Images` to show the images in the center and their respective correct and misclassified labels in the sequence
+
+![GradCam and Misclassified Image]()
+
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference