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README.md

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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()