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title: S13
emoji: 🐨
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 4.28.0
app_file: app.py
pinned: false
license: mit
---
# 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
Thank you
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