Create README.md

README.md

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+---
+license: mit
+tags:
+  - vqvae
+  - image-generation
+  - unsupervised-learning
+  - pytorch
+  - imagenet
+  - generative-model
+datasets:
+  - imagenet-200
+library_name: pytorch
+model-index:
+  - name: VQ-VAE-ImageNet200
+    results:
+      - task:
+          type: image-generation
+          name: Image Generation
+        dataset:
+          name: Tiny ImageNet (ImageNet-200)
+          type: image-classification
+        metrics:
+          - name: FID
+            type: frechet-inception-distance
+            value: 102.87
+---
+
+# VQ-VAE for Tiny ImageNet (ImageNet-200)
+
+This repository contains a **Vector Quantized Variational Autoencoder (VQ-VAE)** trained on the Tiny ImageNet-200 dataset using PyTorch. It is part of an image augmentation and representation learning pipeline for generative modeling and unsupervised learning tasks.
+
+---
+
+## 🧠 Model Details
+
+- **Model Type**: Vector Quantized Variational Autoencoder (VQ-VAE)
+- **Dataset**: Tiny ImageNet (ImageNet-200)
+- **Epochs**: 35  
+- **Latent Space**: Discrete codebook (vector quantization)  
+- **Input Size**: 64×64 RGB  
+- **Loss Function**: Mean Squared Error (MSE) + VQ commitment loss  
+- **Final Training Loss**: ~0.0292  
+- **FID Score**: ~102.87  
+- **Architecture**: 3-layer CNN Encoder & Decoder with quantization bottleneck
+
+---
+
+## 📦 Files
+
+- `generator.pt` — Trained VQ-VAE model weights
+- `loss_curve.png` — Plot of training loss across 35 epochs
+- `fid_score.json` — FID evaluation result on 1000 generated samples
+- `fid_real/` — 1000 real Tiny ImageNet samples used for FID
+- `fid_fake/` — 1000 VQ-VAE reconstructions used for FID
+
+---
+
+## 🔧 Usage
+
+```python
+import torch
+from models.vqvae.model import VQVAE
+
+model = VQVAE()
+model.load_state_dict(torch.load("generator.pt", map_location="cpu"))
+model.eval()