---
tags:
- image-super-resolution
- vision-transformer
- pytorch
- computer-vision
- super-resolution
- lsdir
datasets:
- LSDIR
metrics:
- psnr
---

# ViT-ISR-Tiny: Vision Transformer for ×4 Image Super-Resolution

![PyTorch](https://img.shields.io/badge/Framework-PyTorch-orange)
![PSNR](https://img.shields.io/badge/Test%20PSNR-23.30%20dB-green)
![Params](https://img.shields.io/badge/Parameters-786K-lightgrey)


A Vision Transformer for ×4 image super-resolution — built entirely from scratch in PyTorch, trained on 76,716 real-world images from the LSDIR benchmark, a tiny model with less than million parameters

👉 **[Try the Live Demo](https://huggingface.co/spaces/Sathya77/ViT-ISR-Tiny-LSDIR)**

---

## What it does

Takes a low-resolution image and reconstructs a ×4 higher-resolution version using global self-attention — capturing long-range spatial relationships across the entire image rather than just local neighborhoods like CNNs do.

---

## Architecture

The pipeline: patch embed → transformer blocks → reshape → PixelShuffle upsample → RGB output.

| Component | Details |
|-----------|---------|
| Patch size | 2×2 → 1,024 tokens per image |
| Embedding dim | 64 |
| Transformer blocks | 6 (pre-norm, residual) |
| Attention heads | 4 |
| MLP hidden dim | 256 |
| Upsampling | 3× PixelShuffle ×2 (×8 total) |
| Parameters | ~786K · ~3 MB (fp32) |

**Why pre-norm:** LayerNorm before each sub-layer keeps the residual path clean, stabilizing training in deep stacks.

**Why PixelShuffle:** Learned upsampling — the model decides what to put in new pixels by redistributing channel information into space, rather than stretching with interpolation.

**Why patch size 2:** Finer tokens preserve more spatial detail for reconstruction. Larger patches are cheaper but lose the high-frequency information SR depends on.

---

## Training

| Setting | Value |
|---------|-------|
| Dataset | LSDIR (76,716 train / 4,263 test) |
| Optimizer | AdamW (lr=2e-4) |
| Loss | L1 |
| Mixed precision | fp16 AMP |
| Batch size | 16 |
| Degradation | Bicubic ×4 downscaling |
| Hardware | RTX 4060 Laptop (8GB) |

**Test PSNR: 23.30 dB** — evaluated on the held-out test split (never seen during training or checkpoint selection).

---

## Usage

```python
from huggingface_hub import hf_hub_download
import torch

weights_path = hf_hub_download("Sathya77/ViT-ISR-Tiny-LSDIR", "sr_best.pt")
checkpoint = torch.load(weights_path, map_location="cpu")

model = ImageSRTransformer()   # from model_architecture.py
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
```

---

## Files

| File | Description |
|------|-------------|
| `sr_best.pt` | Best checkpoint (weights + optimizer state) |
| `model_weights.pt` | Weights only — for inference |
| `config.json` | Architecture config |
| `training_config.json` | Training config and results |