Update README.md

README.md

@@ -3,9 +3,91 @@ license: apache-2.0
 language:
 - en
 metrics:
-- accuracy
+- FID
+- IS
 pipeline_tag: unconditional-image-generation
 tags:
-- medical
-- biology
----
+- 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(noise)