---
language: en
license: mit
tags:
  - image-classification
  - computer-vision
  - pytorch
  - cifar10
datasets:
  - cifar10
metrics:
  - accuracy
---

# CIFAR-10 CNN Model

This is a convolutional neural network trained on the CIFAR-10 dataset, achieving 92.59% test accuracy after 100 epochs.

## Model Details
- **Architecture**: 9 convolutional layers with batch normalization, max pooling, and dropout, followed by 3 fully connected layers.
- **Dataset**: CIFAR-10 (10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
- **Training**: 100 epochs, SGD optimizer, CrossEntropyLoss, learning rate scheduling.
- **Accuracy**: 92.59% on the CIFAR-10 test set.

## Usage
Load the model using:

```python
from huggingface_hub import from_pretrained_pytorch
model = from_pretrained_pytorch('chandu1617/CIFAR10-CNN_Model')
```

## Interactive Demo
Try the model in an interactive Gradio UI at [chandu1617/cifar10-cnn-demo](https://huggingface.co/spaces/chandu1617/cifar10-cnn-demo).

## Training Details
- **Optimizer**: SGD with momentum 0.9, weight decay 1e-6.
- **Learning Rate**: Initial 0.01, reduced on plateau (factor 0.1, patience 10, min_lr 0.00001).
- **Data Augmentation**: Color jitter, random perspective, random horizontal flip, normalization.