Aesthetics Quality Assessment On Ava

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
MP_adam	83.0%	Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
A-Lamp	82.5%	A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
Pool-3FC	81.7%	Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
NIMA	81.5%	NIMA: Neural Image Assessment
MTRLCNN	79.1%	Deep Aesthetic Quality Assessment with Semantic Information
MNA-CNN	77.4%	Composition-Preserving Deep Photo Aesthetics Assessment
ADB-CNN	77.3%	Photo Aesthetics Ranking Network with Attributes and Content Adaptation
DMA-Net	75.4%	Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation
Hand-crafted features	68.0%	-

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Aesthetics Quality Assessment On Ava | SOTA | HyperAI