Abhishek2714C/Cancer_Image_Generation

Model Card for DCGAN (PyTorch)

Model Details

Model Description

This is a Deep Convolutional Generative Adversarial Network (DCGAN) implemented in PyTorch.
It is trained to generate synthetic images that resemble the target dataset distribution.

• Developed by: Abhishek C.
• Funded by [optional]: Self Funded
• Shared by: None
• Model type: Generative Adversarial Network (DCGAN)
• Language(s): N/A (Image generation)
• License: Apache-2.0
• Finetuned from model [optional]: Not applicable (trained from scratch)

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Uses

Direct Use

• Generating synthetic images from random noise vectors (z ~ N(0,1)).
• Data augmentation for research and experimentation.
• Educational purposes to study GAN training and generative modeling.

Downstream Use

• Fine-tuning the discriminator or generator on domain-specific datasets.
• Using the pretrained generator as an initialization for conditional GANs.

Out-of-Scope Use

• Medical or safety-critical applications without validation.
• Misuse for generating harmful or misleading content.

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Bias, Risks, and Limitations

• Generated images may contain artifacts if training is insufficient.
• Quality depends heavily on dataset diversity and size.
• Model may amplify dataset biases.

Recommendations

• Always evaluate generated images before downstream use.
• Do not use in decision-critical tasks.
• Use larger datasets for stable performance.

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How to Get Started with the Model

import torch
from torch import nn

# Load pretrained generator (example structure)
class Generator(nn.Module):
    def __init__(self, nz=100, ngf=64, nc=3):
        super().__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf*8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf*8),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf*4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf*2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*2, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

# Example usage
netG = Generator()
noise = torch.randn(16, 100, 1, 1)
fake_images = netG(noise)