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Cancer Image Generation

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.

Sourcehuggingface.co/Abhishek2714C/Cancer_Image_Generation/tree/9db5085ce781064fd4e0d3d66a0501bb7c11dd3f
KindOtherVersionRevision 9db5085Cgrade

Model source

Kind
Other
File size
2.66–50.5 MB
Version
vRevision 9db5085
Updated
Sep 27, 2026
Downloads
0
Source
Hugging Face

Source description

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)

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.

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.

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)

Sources

1 source
Hugging FacePrimaryVersion 9db5085ce781064fd4e0d3d66a0501bb7c11dd3f
huggingface.co/Abhishek2714C/Cancer_Image_Generation/tree/9db5085ce781064fd4e0d3d66a0501bb7c11dd3f

Verified Aug 31

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Model artifacts

2 artifacts

discriminator.pth

pth · 2.66 MB · SHA-256 a0b984cf93ab…3d29 · Hugging Face

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generator.pth

pth · 50.5 MB · SHA-256 b3235ec06cea…821d · Hugging Face

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Source excerpts

2 excerpts
ReadmeAug 31huggingface.co/Abhishek2714C/Cancer_Image_Generation

--- license: apache-2.0 language: - en metrics: - FID - IS pipeline_tag: unconditional-image-generation tags: - generative-adversarial-network - pytorch - dcgan - deep-learning --- # 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) --- ## 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. --- ## 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. --- ## How to Get Started with the Model ```python 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(n...

NoteAug 31huggingface.co/Abhishek2714C/Cancer_Image_Generation

Source context: 0 downloads · 1 likes · Pipeline unconditional-image-generation · Repo Abhishek2714C/Cancer_Image_Generation