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README.MD

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+---
+license: mit
+tags:
+  - vqvae
+  - image-generation
+  - unsupervised-learning
+  - pytorch
+  - cifar10
+  - generative-model
+datasets:
+  - cifar10
+library_name: pytorch
+model-index:
+  - name: VQ-VAE-CIFAR10
+    results: []
+---
+
+# VQ-VAE for CIFAR-10
+
+This is a **Vector Quantized Variational Autoencoder (VQ-VAE)** trained on the CIFAR-10 dataset using PyTorch. It is part of an image augmentation pipeline for generative modeling and unsupervised learning research.
+
+## 🧠 Model Details
+
+- **Model Type**: VQ-VAE
+- **Dataset**: CIFAR-10
+- **Epochs**: 35  
+- **Latent Space**: Discrete (quantized vectors)
+- **Input Size**: 64×64  
+- **Reconstruction Loss**: MSE-based  
+- **Implementation**: Custom PyTorch, 3-layer Conv Encoder/Decoder  
+- **FID Score**: **71.11**  
+- **Loss Curve**: [`loss_curve.png`](./loss_curve.png)
+
+> This model was trained to learn a compact representation of CIFAR-10 images via vector quantization and used for downstream data augmentation.
+
+## 📁 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 samples 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()