CIFAR10-CNN_Model

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:

from huggingface_hub import from_pretrained_pytorch
model = from_pretrained_pytorch('ChandraPrakashBathula/CIFAR10-CNN_Model')

Interactive Demo

Try the model in an interactive Gradio UI at ChandraPrakashBathula/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.