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cGAN-MNIST

cgan
conditional-gan
generative-adversarial-network
image-generation
deep-learning
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metadata
tags:
  - cgan
  - conditional-gan
  - generative-adversarial-network
  - image-generation
  - deep-learning
datasets:
  - MNIST
license: mit

Conditional GAN Model Card

Model Description

This is a Conditional GAN (cGAN) trained on the MNIST dataset to generate realistic 28x28 grayscale images of handwritten digits. The model leverages label information to guide image generation and was developed as part of the Generative AI course.

Training Details

  • Dataset: MNIST
  • Subset Size: 60,000 images
  • Image Size: 28x28
  • Number of Channels: 1 (grayscale)
  • Latent Dimension: 100
  • Generator Feature Map Size: 64
  • Discriminator Feature Map Size: 64
  • Batch Size: 128
  • Epochs: 50
  • Learning Rate: 0.0002
  • Beta1: 0.5
  • Weight Decay: 0
  • Optimizer: Adam
  • Hardware: CUDA-enabled GPU
  • Logging: Weights and Biases (wandb)

Weights and Biases Run

The training process was tracked using Weights and Biases. You can view the full training logs and metrics here.

Usage

Downloading the Model from the Hub

You can download the model checkpoint directly from the hub using the huggingface_hub library:

from huggingface_hub import hf_hub_download

# Download the model checkpoint from the hub
checkpoint_path = hf_hub_download(repo_id="hussamalafandi/cGAN-MNIST", filename="generator.pth")

Loading the Model Locally 

import torch
from c_gan import Generator

# Load the configuration
config = {
    "latent_dim": 100,
    "ngf": 64,
    "nc": 1,
    "num_classes": 10,
    "embed_dim": 50
}

# Initialize the generator
generator = Generator(config)

# Load the downloaded checkpoint (or a local path)
generator.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cuda' if torch.cuda.is_available() else 'cpu')))

# Set the model to evaluation mode
generator.eval()

# Example: Generate an image
latent_vector = torch.randn(1, config["latent_dim"], 1, 1)  # Batch size of 1

if torch.cuda.is_available():
    latent_vector = latent_vector.cuda()
    generator = generator.cuda()

generated_image = generator(latent_vector, torch.tensor([7]))  # Example label: 7

Example Results

generate image

Resources