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--- |
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title: S13 |
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emoji: 🐨 |
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colorFrom: indigo |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 4.28.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# Pytorch LIghtning Model Trained for CIFAR10 |
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- This application showcases the inference capabilities of a model trained on the CIFAR dataset. |
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- 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). |
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- 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. |
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- The model is coded using PyTorch Lightning 2.2.2. |
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Mentioned below is the link for Training Repository [Training Repo Link](https://github.com/Shivdutta/ERA2-Session13) |
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- Following the training process, the model is saved locally and then uploaded to Gradio Spaces. |
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- Attached below is the link to [download model file](https://huggingface.co/spaces/Shivdutta/S13/blob/main/model.pth) |
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- This app has two features : |
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- **GradCam:** |
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" - To visualize the specific regions of the image that the model focuses on during inference |
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- This insight can guide the development of augmentation strategies aimed at enhancing model accuracy" |
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- **Misclassified Image:** |
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- Despite achieving a test accuracy of over 85% during model training, there remained a 12% misclassification rate for certain images. |
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- This feature facilitates the visualization of misclassified images alongside their correct and incorrect labels. |
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- Such visualization aids in devising strategies to enhance accuracy, especially for specific classes." |
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## Usage: |
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### GradCam |
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- Upload an image or choose from predefined examples. |
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- Adjust the opacity. |
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- Specify the number of top classes you wish to view. |
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- Click the 'Submit' button to generate the results." |
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### Misclassified Images |
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- Select the number of misclassified images you want to see |
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- Click on `Display Misclassified Images` to show the images in the center and their respective correct and misclassified labels in the sequence |
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Thank you |
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