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Pre-converted ONNX models for No-Reference Image Quality Assessment (NR-IQA), exported from IQA-PyTorch and ready for deployment with ONNX Runtime (no PyTorch dependency needed).
Modellquelle
Quellenbeschreibung
Pre-converted ONNX models for No-Reference Image Quality Assessment (NR-IQA), exported from IQA-PyTorch and ready for deployment with ONNX Runtime (no PyTorch dependency needed).
Quellen
1 QuelleVerifiziert 28. Sept.
Modellartefakte
8 ArtefakteQuellenauszüge
2 Auszüge| Model | Input Size | Files | Total Size | Score Range | Description |
|---|
| LIQE | 224×224 | clip_model.onnx + liqe_model.onnx + text_features.json | ~675 MB | 1–5 | CLIP-based learned quality & scene & distortion assessment |
| DBCNN | 224×224 | dbcnn_model.onnx | ~59 MB | 0–100 | Double-blind CNN with VGG16 + SCNN bilinear pooling |
| HyperIQA | 224×224 | hyperiqa_model.onnx | ~105 MB | 0–1 | Hyper-network based quality prediction on ResNet50 |
| MANIQA | 224×224 | maniqa_model.onnx | ~437 MB | continuous | Multi-dimension Attention Network with ViT backbone |
| MUSIQ | 224×224 | musiq_model.onnx | ~104 MB | 0–100 | Multi-scale Image Quality Transformer (single-scale export) |
| TReS | 224×224 | tres_model.onnx | ~575 MB | 0–100 | Transformer + ResNet ensemble with relative ranking |
| CLIPIQA | 224×224 | clipiqa_model.onnx | ~146 MB | 0–1 | CLIP-IQA+ with learned prompts and antialias RN50 |
Most models use external data format (
.onnx+.onnx.data). Both files must be in the same directory for loading.
import onnxruntime as ort
import numpy as np
from PIL import Image
# Load model
sess = ort.InferenceSession("dbcnn_model.onnx")
# Preprocess: resize to 224x224, normalize with ImageNet mean/std
img = Image.open("test.jpg").convert("RGB").resize((224, 224))
x = np.array(img).astype(np.float32) / 255.0
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
x = (x - mean) / std
x = x.transpose(2, 0, 1)[np.newaxis] # (1, 3, 224, 224)
# Run inference
score = sess.run(None, {"input": x.astype(np.float32)})[0]
print(f"Quality score: {score.item():.4f}")
| Model | Normalization | Notes |
|---|---|---|
| DBCNN, HyperIQA, TReS | ImageNet: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | Standard ImageNet |
| MANIQA | Inception: mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5] | |
| MUSIQ | [-1, 1]: (x - 0.5) / 0.5 | Equivalent to Inception |
| CLIPIQA | CLIP: mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711] | |
| LIQE | CLIP: same as CLIPIQA | Two-stage: CLIP encoder → LIQE scorer |
For multi-crop inference (recommended for HyperIQA, MANIQA, TReS), extract multiple 224×224 crops from the full-resolution image and average the scores.
LIQE requires a two-stage pipeline:
import json
import onnxruntime as ort
import numpy as np
# Load both models and text features
clip_sess = ort.InferenceSession("clip_model.onnx")
liqe_sess = ort.InferenceSession("liqe_model.onnx")
with open("text_features.json") as f:
text_features = np.array(json.load(f), dtype=np.float32)
# Step 1: Extract image features with CLIP
img_input = preprocess_clip(image) # (1, 3, 224, 224)
img_features = clip_sess.run(None, {"input": img_input})[0]
# Step 2: Score with LIQE
score = liqe_sess.run(None, {
"image_features": img_features,
"text_features": text_features
})[0]
All models verified against PyTorch source (absolute difference < 0.01):
| Model | PyTorch Score | ONNX Score | Abs Diff |
|---|---|---|---|
| LIQE | — | — | < 0.001 |
| DBCNN | — | — | 0.000011 |
| HyperIQA | — | — | 0.00000003 |
| MANIQA | — | — | 0.000019 |
| MUSIQ | — | — | 0.000025 |
| TReS | — | — | 0.000439 |
| CLIPIQA | — | — | 0.000002 |
| Model | Paper | Conference | ONNX Files | Export Notes |
|---|---|---|---|---|
| LIQE | Blind Image Quality Assessment via Vision-Language Correspondence | CVPR 2023 | clip_model.onnx liqe_model.onnx text_features.json | Two-stage: CLIP ViT-B/32 image encoder + LIQE scoring head; text features pre-encoded to JSON |
| DBCNN | Blind Image Quality Assessment Using A Deep Bilinear CNN | IEEE TCSVT 2020 | dbcnn_model.onnx | VGG16 + SCNN with bilinear pooling; both sub-networks exported as single model |
| HyperIQA |
clip_model.onnx # LIQE CLIP image encoder
clip_model.onnx.data
liqe_model.onnx # LIQE scoring head
liqe_model.onnx.data
text_features.json # LIQE pre-encoded text prompts
dbcnn_model.onnx # DBCNN
dbcnn_model.onnx.data
hyperiqa_model.onnx # HyperIQA
hyperiqa_model.onnx.data
maniqa_model.onnx # MANIQA (single file, no .data)
musiq_model.onnx # MUSIQ
musiq_model.onnx.data
tres_model.onnx # TReS
tres_model.onnx.data
clipiqa_model.onnx # CLIPIQA+
clipiqa_model.onnx.data
If you use these models, please cite the original papers and the IQA-PyTorch toolbox:
@article{chaofeng2022iqapytorch,
title={IQA-PyTorch: PyTorch Toolbox for Image Quality Assessment},
author={Chaofeng Chen and Jiadi Mo},
year={2022},
journal={arXiv preprint arXiv:2208.14818}
}
Apache 2.0. Original model weights are subject to their respective licenses.
dbcnn_model.onnx
onnx · 99,8 KB · SHA-256 2a679a6264bf…bc3f · Hugging Face
--- license: apache-2.0 tags: - image-quality-assessment - onnx - iqa - no-reference language: - en library_name: onnxruntime pipeline_tag: image-classification --- # IQA ONNX Models Pre-converted ONNX models for **No-Reference Image Quality Assessment (NR-IQA)**, exported from [IQA-PyTorch](https://github.com/chaofengc/IQA-PyTorch) and ready for deployment with ONNX Runtime (no PyTorch dependency needed). ## Models | Model | Input Size | Files | Total Size | Score Range | Description | |-------|-----------|-------|------------|-------------|-------------| | **[LIQE](https://arxiv.org/abs/2304.00451)** | 224×224 | `clip_model.onnx` + `liqe_model.onnx` + `text_features.json` | ~675 MB | 1–5 | CLIP-based learned quality & scene & distortion assessment | | **[DBCNN](https://arxiv.org/abs/1907.02665)** | 224×224 | `dbcnn_model.onnx` | ~59 MB | 0–100 | Double-blind CNN with VGG16 + SCNN bilinear pooling | | **[HyperIQA](https://arxiv.org/abs/2003.08932)** | 224×224 | `hyperiqa_model.onnx` | ~105 MB | 0–1 | Hyper-network based quality prediction on ResNet50 | | **[MANIQA](https://arxiv.org/abs/2204.08958)** | 224×224 | `maniqa_model.onnx` | ~437 MB | continuous | Multi-dimension Attention Network with ViT backbone | | **[MUSIQ](https://arxiv.org/abs/2108.05997)** | 224×224 | `musiq_model.onnx` | ~104 MB | 0–100 | Multi-scale Image Quality Transformer (single-scale export) | | **[TReS](https://arxiv.org/abs/2108.06858)** | 224×224 | `tres_model.onnx` | ~575 MB | 0–100 | Transformer + ResNet ensemble with relative ranking | | **[CLIPIQA](https://arxiv.org/abs/2207.12396)** | 224×224 | `clipiqa_model.onnx` | ~146 MB | 0–1 | CLIP-IQA+ with learned prompts and antialias RN50 | > Most models use external data format (`.onnx` + `.onnx.data`). **Both files must be in the same directory** for loading. ## Quick Start ```python import onnxruntime as ort import numpy as np from PIL import Image # Load model sess = ort.InferenceSession("dbcnn_model.onnx") # Preprocess: resize to 224x224, normalize with ImageNet mean/std img = Image.open("test.jpg").convert("RGB").resize((224, 224)) x = np.array(img).astype(np.float32) / 255.0 mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) x = (x - mean) / std x = x.transpose(2, 0, 1)[np.newaxis] # (1, 3, 224, 224) # Run inference score = sess.run(None, {"input": x.astype(np.float32)})[0] print(f"...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Library onnxruntime · Repo 86Cao/IQA-ONNX-Models
| Blindly Assess Image Quality in the Wild Boosted by A Large-scale Database |
| CVPR 2020 |
hyperiqa_model.onnx |
| ResNet50 backbone with hyper-network; exported forward patch path only |
| MANIQA | MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment | CVPR 2022 NTIRE | maniqa_model.onnx | ViT-B/8 backbone; single-file export (no external data) |
| MUSIQ | MUSIQ: Multi-scale Image Quality Transformer | ICCV 2021 | musiq_model.onnx | Simplified to single-scale 224×224 input (original uses multi-scale patches) |
| TReS | No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency | WACV 2022 | tres_model.onnx | Eval-only path; dual-path consistency and flip branches removed |
| CLIPIQA | Exploring CLIP for Assessing the Look and Feel of Images | AAAI 2023 | clipiqa_model.onnx | CLIP-IQA+ variant with learned prompts baked into the model; antialias RN50 backbone |