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sohumgautam/
cifar_gan_model

Name: cifar_gan_model
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ConditionalGAN

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Conditional GAN for CIFAR-10

This repository contains a trained Conditional Generative Adversarial Network (CGAN) for generating CIFAR-10 images.

Model Details

Architecture: Conditional GAN
Dataset: CIFAR-10
Image Size: 32x32x3
Latent Dimension: 100
Number of Classes: 10

Usage

python

import torch
from gan_model import ConditionalGenerator, ConditionalDiscriminator

# Load the models
generator = ConditionalGenerator(latent_dim=100, num_classes=10)
discriminator = ConditionalDiscriminator(num_classes=10)

generator.load_state_dict(torch.load("generator.pth"))
discriminator.load_state_dict(torch.load("discriminator.pth"))

generator.eval()
discriminator.eval()

# Generate images
noise = torch.randn(1, 100)
label = torch.tensor([0])  # Class 0 (airplane)
fake_image = generator(noise, label)

# Evaluate with discriminator
disc_output = discriminator(fake_image, label)

Classes

airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck

Files

generator.pth: Trained generator weights
discriminator.pth: Trained discriminator weights
gan_model.py: Model architecture definitions
config.json: Model configuration

sohumgautam/cifar_gan_model