mnist_vqvae

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

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

import torch
from models.vqvae.model import VQVAE

model = VQVAE()
model.load_state_dict(torch.load("generator.pt", map_location="cpu"))
model.eval()