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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.
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