metadata

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

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()