# IQA-PyTorch Toolbox — All-in-One

## Model Info

- **GitHub:** <https://github.com/chaofengc/IQA-PyTorch>
- **PyPI:** `pip install pyiqa`
- **License:** Apache 2.0
- **Contents:** 30+ IQA metrics including MUSIQ, TOPIQ, BRISQUE, NIQE, LPIPS, FID, NIMA, DBCNN, CLIP-IQA, LIQE, Q-Align (Q-ReAlign), and more
- **Latest updates (2026):**
  - Jun 2026: Added Q-ReAlign (Qwen3.5-VL backbone) — 3 sizes: mini (0.8B), lite (4B), pro (9B)
  - May 2026: Added FGResQ
  - Dec 2025: Added DMM, MACLIP, AFINE
  - Jan 2025: Added QualiCLIP variants
- **Results calibrated:** Against official MATLAB scripts when available
- **GPU accelerated:** PyTorch implementations are faster than MATLAB counterparts

## Why It Matters

This toolbox lets you test multiple IQA models on your wedding photos without installing each separately. Useful for benchmarking and comparison. It's the single easiest way to run BRISQUE + NIQE + MUSIQ + other metrics in one codebase.

```bash
# List all available metrics
pyiqa -ls

# Test multiple metrics on a directory
pyiqa musiq niqe brisque -t ./wedding-photos/ --device cuda

# Python API — all metrics in one import
import pyiqa
import torch

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

# Create any metric
brisque = pyiqa.create_metric('brisque', device=device)
musiq = pyiqa.create_metric('musiq', device=device)
niqe = pyiqa.create_metric('niqe', device=device)

# Check scoring direction
print(f"BRISQUE lower_better: {brisque.lower_better}")  # True
print(f"MUSIQ lower_better: {musiq.lower_better}")      # False (higher = better)
print(f"NIQE lower_better: {niqe.lower_better}")        # True

# Batch inference
import glob
for path in glob.glob('./wedding-photos/*.jpg'):
    score_b = brisque(path)
    score_m = musiq(path)
    score_n = niqe(path)
```

## Available Metrics (relevant to wedding photo sorting)

| Metric | Type | Score Direction | GPU Required | Notes |
|--------|------|----------------|--------------|-------|
| brisque | NR-IQA | Lower better | No (CPU) | Blur/noise detection |
| niqe | NR-IQA | Lower better | No (CPU) | Blur/noise detection, opinion-unaware |
| musiq | NR-IQA (Transformer) | Higher better | Yes (~4GB) | Aesthetic + technical combined |
| toqip | NR-IQA (Transformer) | Higher better | Yes (~2GB) | Modern, self-supervised |
| clip_iqa | NR-IQA (CLIP) | Higher better | Yes (~2GB) | CLIP-based aesthetic |
| liqe | NR-IQA (CLIP) | Higher better | Yes (~2GB) | CLIP-based quality |
| lpips | FR-IQA (Perceptual) | Lower better | Yes (~1GB) | Needs reference image |
| fid | FR-IQA (Distribution) | Lower better | Yes (~5GB) | Needs reference dataset |
| nima | NR-IQA (CNN) | Higher better | Yes (~1GB) | Neural IMage Assessment |
| dbcnn | FR-IQA (CNN) | Higher better | Yes (~2GB) | Needs reference image |