hussamalafandi/
DCGAN_CelebA

Name: DCGAN_CelebA
Availability: InStock

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dcgan

generative-adversarial-network

GPU deploy isn't supported for this model type.

DCGAN Model Card

Model Description

This is a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the CelebA dataset to generate realistic 64x64 RGB images of human faces. The model was developed as part of the Generative AI course.

Training Details

Dataset: CelebA
Subset Size: 50,000 images
Image Size: 64x64
Number of Channels: 3 (RGB)
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

Loading the Model

To load the trained model, use the following code snippet:

python

import torch
from dcgan import Generator

# Load the configuration
config = {
    "latent_dim": 100,
    "ngf": 64,
    "nc": 3
}

# Initialize the generator
generator = Generator(config)

# Load the trained weights
model_path = "./dcgan_celeba.pth"

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)

Example Results

Resources

Course Repository: Generative AI Course
WandB Run: DCGAN_CelebA Run

hussamalafandi/DCGAN_CelebA