Kareem Horstink
66 lines
2.8 KiB
Markdown
66 lines
2.8 KiB
Markdown
# IQA-PyTorch Toolbox — All-in-One
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## Model Info
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- **GitHub:** <https://github.com/chaofengc/IQA-PyTorch>
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- **PyPI:** `pip install pyiqa`
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- **License:** Apache 2.0
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- **Contents:** 30+ IQA metrics including MUSIQ, TOPIQ, BRISQUE, NIQE, LPIPS, FID, NIMA, DBCNN, CLIP-IQA, LIQE, Q-Align (Q-ReAlign), and more
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- **Latest updates (2026):**
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- Jun 2026: Added Q-ReAlign (Qwen3.5-VL backbone) — 3 sizes: mini (0.8B), lite (4B), pro (9B)
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- May 2026: Added FGResQ
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- Dec 2025: Added DMM, MACLIP, AFINE
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- Jan 2025: Added QualiCLIP variants
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- **Results calibrated:** Against official MATLAB scripts when available
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- **GPU accelerated:** PyTorch implementations are faster than MATLAB counterparts
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## Why It Matters
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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.
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```bash
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# List all available metrics
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pyiqa -ls
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# Test multiple metrics on a directory
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pyiqa musiq niqe brisque -t ./wedding-photos/ --device cuda
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# Python API — all metrics in one import
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import pyiqa
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import torch
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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# Create any metric
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brisque = pyiqa.create_metric('brisque', device=device)
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musiq = pyiqa.create_metric('musiq', device=device)
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niqe = pyiqa.create_metric('niqe', device=device)
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# Check scoring direction
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print(f"BRISQUE lower_better: {brisque.lower_better}") # True
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print(f"MUSIQ lower_better: {musiq.lower_better}") # False (higher = better)
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print(f"NIQE lower_better: {niqe.lower_better}") # True
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# Batch inference
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import glob
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for path in glob.glob('./wedding-photos/*.jpg'):
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score_b = brisque(path)
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score_m = musiq(path)
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score_n = niqe(path)
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```
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## Available Metrics (relevant to wedding photo sorting)
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| Metric | Type | Score Direction | GPU Required | Notes |
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|--------|------|----------------|--------------|-------|
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| brisque | NR-IQA | Lower better | No (CPU) | Blur/noise detection |
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| niqe | NR-IQA | Lower better | No (CPU) | Blur/noise detection, opinion-unaware |
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| musiq | NR-IQA (Transformer) | Higher better | Yes (~4GB) | Aesthetic + technical combined |
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| toqip | NR-IQA (Transformer) | Higher better | Yes (~2GB) | Modern, self-supervised |
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| clip_iqa | NR-IQA (CLIP) | Higher better | Yes (~2GB) | CLIP-based aesthetic |
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| liqe | NR-IQA (CLIP) | Higher better | Yes (~2GB) | CLIP-based quality |
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| lpips | FR-IQA (Perceptual) | Lower better | Yes (~1GB) | Needs reference image |
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| fid | FR-IQA (Distribution) | Lower better | Yes (~5GB) | Needs reference dataset |
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| nima | NR-IQA (CNN) | Higher better | Yes (~1GB) | Neural IMage Assessment |
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| dbcnn | FR-IQA (CNN) | Higher better | Yes (~2GB) | Needs reference image |
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