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# GAN-MNIST
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A simple DCGAN trained on the MNIST dataset using PyTorch for data augmentation
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- Latent Dim: 100
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- Discriminator & Generator: 3-layer CNN
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- FID Score: 24.16
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## Usage
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```python
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from torch import load
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from models.gan.model import Generator
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model = Generator(latent_dim=100, img_channels=1)
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model.load_state_dict(load("generator.pt"))
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model.eval()
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---
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library_name: pytorch
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tags:
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- gan
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- mnist
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- image-generation
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- computer-vision
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- pytorch
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license: mit
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datasets:
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- mnist
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model-index:
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- name: DCGAN-MNIST
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results: []
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---
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# GAN-MNIST
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A simple yet effective **DCGAN** trained on the **MNIST** dataset using PyTorch, designed for **data augmentation** experiments.
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## 🧠 Model Details
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- **Architecture:** Deep Convolutional GAN (DCGAN)
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- **Generator/Discriminator:** 3-layer CNN
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- **Latent Dimension:** 100
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- **Epochs Trained:** 20
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- **Final FID Score:** 24.16
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- **Image Size:** 28×28 grayscale
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## 📦 Usage
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```python
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from torch import load
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from models.gan.model import Generator
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# Load model
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model = Generator(latent_dim=100, img_channels=1)
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model.load_state_dict(load("generator.pt"))
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model.eval()
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