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---
license: mit
tags:
- vqvae
- image-generation
- unsupervised-learning
- pytorch
- mnist
- generative-model
datasets:
- mnist
library_name: pytorch
model-index:
- name: VQ-VAE-MNIST
results:
- task:
type: image-generation
name: Image Generation
dataset:
name: MNIST
type: image-classification
metrics:
- name: FID
type: frechet-inception-distance
value: 53.21
---
# VQ-VAE for MNIST
This is a **Vector Quantized Variational Autoencoder (VQ-VAE)** trained on the MNIST dataset using PyTorch. The model compresses and reconstructs grayscale handwritten digits and is used as part of an image augmentation and generative modeling pipeline.
## 🧠 Model Details
- **Model Type**: VQ-VAE
- **Dataset**: MNIST
- **Epochs**: 35
- **Latent Space**: Discrete (quantized vectors)
- **Input Size**: 64×64 (resized and converted to RGB)
- **Reconstruction Loss**: MSE-based
- **Implementation**: Custom PyTorch with 3-layer Conv Encoder/Decoder
- **FID Score**: **53.21**
- **Loss Curve**: [`loss_curve.png`](./loss_curve.png)
> This model learns compressed representations of digit images using vector quantization. The reconstructions can be used for augmentation or generative downstream tasks.
## 📁 Files
- `generator.pt`: Trained VQ-VAE model weights.
- `loss_curve.png`: Visual plot of training loss across 35 epochs.
- `fid_score.json`: Stored Fréchet Inception Distance (FID) evaluation result.
- `fid_real/` and `fid_fake/`: 1000 real and generated images used for FID computation.
## 📦 How to Use
```python
import torch
from models.vqvae.model import VQVAE
model = VQVAE()
model.load_state_dict(torch.load("generator.pt", map_location="cpu"))
model.eval()
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