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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.

Quellehuggingface.co/Abhishek2714C/Cancer_Image_Generation/tree/9db5085ce781064fd4e0d3d66a0501bb7c11dd3f

ArtOtherVersionRevision 9db5085CBewertung

Modellquelle

Art: Other
Dateigröße: 2,66–50,5 MB
Version: vRevision 9db5085
Aktualisiert: 27. Sept. 2026
Downloads: 0
Quelle: Hugging Face

Quellenbeschreibung

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)

Quellen

1 Quelle

Hugging FacePrimärVersion 9db5085ce781064fd4e0d3d66a0501bb7c11dd3f

huggingface.co/Abhishek2714C/Cancer_Image_Generation/tree/9db5085ce781064fd4e0d3d66a0501bb7c11dd3f

Verifiziert 31. Aug.

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Modellartefakte

2 Artefakte

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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Quellenauszüge

2 Auszüge

Readme31. Aug.huggingface.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...

Note31. Aug.huggingface.co/Abhishek2714C/Cancer_Image_Generation

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