AAL Plus Image Quality Assessment
Overview
Highlights
- Provides objective quantitative metrics for visual fidelity assessment
- Optimized for detecting image artifacts and noise levels
- Ideal for automated image processing and restoration pipelines
- Permissive Apache-2.0 license for easy commercial integration
Usage
# Install Hugging Face transformers
pip install transformers torch# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("LoliRimuru/AAL-Plus_Image_Quality_Assessment")
tokenizer = AutoTokenizer.from_pretrained("LoliRimuru/AAL-Plus_Image_Quality_Assessment")Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance：Before downloading, install huggingface_hub with:
pip install -U huggingface_hubCLI Download
Download the full repository
huggingface-cli download LoliRimuru/AAL-Plus_Image_Quality_AssessmentDownload a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download LoliRimuru/AAL-Plus_Image_Quality_Assessment config.json --local-dir ./dirSee the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('LoliRimuru/AAL-Plus_Image_Quality_Assessment')Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/LoliRimuru/AAL-Plus_Image_Quality_AssessmentTo skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/LoliRimuru/AAL-Plus_Image_Quality_AssessmentModel files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
pip install -U transformers torchLoad the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('LoliRimuru/AAL-Plus_Image_Quality_Assessment')
tokenizer = AutoTokenizer.from_pretrained('LoliRimuru/AAL-Plus_Image_Quality_Assessment')Full Documentation
---
license: apache-2.0
tags:
- image-classification
- qualits-assessment
- compression-artifacts
- art
pipeline_tag: image-classification
---
AAL-Plus Image Quality Assessment
A lightweight model that predicts compression quality levels and detects artifacts across multiple image formats (JPEG, WebP, AVIF, JXL). Trained to identify subtle compression artifacts that indicate the quality level used during image encoding.
📊 Model Performance
Based on validation results from the final training epochs:
| Metric | Value |
|--------|-------|
| Overall Validation Accuracy | 97.1% |
| Overall Validation Loss | 0.0006 |
| Training Accuracy | 95.6% |
Per-Format Accuracy
| Format | Validation Acc | Training Acc | Quality Range |
|--------|----------------|--------------|---------------|
| JPEG | 99.4% | 98.9% | 0-100 |
| WebP | 97.0% | 95.5% | 0-100 |
| AVIF | 97.1% | 95.3% | 0-100 |
| JXL | 94.8% | 92.6% | 0-100 |
*Accuracy measured as predictions within ±5% range of actual quality values*
🎯 Key Features
- Multi-format Support: Detects artifacts in JPEG, WebP, AVIF, and JXL formats
- Lightweight Architecture: Only ~2M parameters (~8MB model size)
- High Precision: 99.4% on JPEG
- Fast Inference: Optimized for real-time processing even on very weak HW using either CPU or GPU
- Format-agnostic: Single model handles all compression types
Limitations
1. Resolution Dependency: Model trained on 512x512 crops; very small images may yield reduced accuracy
2. Quality Range: Model assumes standard quality ranges; non-standard encoders may produce different results
3. Image Resizing: Trying to detect compression artifacs in images which were compressed and than converted to lossless format and resized may fail or be much less accurate as these artifacts are washed by the resizing algorithms.
Environmental Impact
- Training: ~12 GPU hours on RTX 5090
- Model Size: 8MB (reduces storage and bandwidth costs)