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---
title: Cifar10
emoji: 🔥
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 4.38.1
app_file: app.py
fullWidth: true
models:
- >-
https://huggingface.co/spaces/Shilpaj/cifar10/blob/main/epoch%3D23-step%3D2112.ckpt
datasets:
- CIFAR10
pinned: false
license: mit
---
# Model Trained for CIFAR10
- This Application demonstrate the inference side of the model trained on the CIFAR dataset
- David C's model architecture is recreated and trained on CIFAR10 dataset to achieve the accuracy of 90+% within 24 epochs
- Once Cycle Policy is used to speed up the training process
- The model is coded using PyTorch Lightning. Mentioned below is the link for Training Repository where you can check the code for model training and tracked metrics
##### [Training Repo Link](https://github.com/Shilpaj1994/ERA/tree/master/Session12)
- After the Training, model checkpoint is stored on the system and uploaded to Gradio Spaces. Attached below is the link to download model file
##### [Download Model File](https://huggingface.co/spaces/Shilpaj/cifar10/resolve/main/epoch%3D23-step%3D2112.ckpt)
- This app has four features in four tabs:
- **GradCam:**
- To visualize which portion of the image model is actually looking at while inferencing on the image
- Using this, we can come up with an augmentation strategy that can improve the model accuracy
- **Misclassified Image:**
- While training the model, though the `test accuracy` was 90+%, there were still 10% images which were misclassified
- The feature helps to visualize those images with their respective correct and incorrect labels
- This can be used to come up with a strategy to improve accuracy for a particular class
- **Feature Map Visualization:**
- There are 6 block in this model
- Each block has 2 or 3 convolutional layers
- The output of specific kernel are visualized for first convolutional layer of all 6 blocks
- **Kernel Visualization:**
- In the first layer of each of the six blocks, there are kernels which are creating the feature maps
- Some of those kernels are visualized in this section
**Dependencies:**
- Python Version: 3.x
- PyTorch Lightning: 2.0.6
## Usage:
### GradCam
- Upload an image or select from example
- Select how much percentage of original image should be overlapped on what actually model is looking at in the image
- Select the number of top classes you want to see
- Select the block number for which first convolutional layer's activation you want to see
- Click on the `Submit` button to see 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
### Feature Map Visualization
- Upload an image
- Select the kernel number
- Click on `Visualize FeatureMaps` to see the feature maps created by the kernel number for first convolutional layer in each of the six blocks
### Kernel Visualization
- Select the block number from which first convolutional layer's kernel to be visualized
- Click on `Visualize Kernels` to see the kernels
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