Edit model card

Welcome to experience the open source aesthetic scoring and data cleaning toolset (AAV) developed by Laxhar Dream Lab for XL training. which can be used for preprocessing of large dataset training, aesthetic scoring and automatic annotation of quality words, if it is useful to you, welcome to add a ❀ to the project.

The dataset comes from 200,000 manually selected anime images, and the scoring dimensions include both picture quality and composition.

The model scoring using dual-model supervisory architecture (DMSA)

The model structure can be seen in the following figure:


[Model Basic Information]

Parametric quantities : 1.1b+110m picture+composition vit+grn

Scoring range : -1~1

The current quality cue word is divided into 5 levels: masterpiece, high quality, normal quality, low quality, worst quality


[Model Advantages]

√ Faster recognition speed In a standard environment, the AAV model can evaluate 10,000 images in 30 minutes with high accuracy, for one million anime picture scoring also need only 50h!

√ More flexible compositional judgment Adopt compositional confidence function for score-balance.

Composition confidence is a measure of whether the composition of an image conforms to human aesthetic preferences and rules. Composition refers to how the elements in an image are arranged and combined to achieve a certain visual effect and express intent.


Composition confidence can be measured by compositional patterns, compositional partitioning & visual saliency in AAV.

[How to use]

You can have the actual experience of evaluating the model with Project anime-thetic in HFspace! Examples have been provided in HFspace based on different rating scales, or you can use your own uploaded images.

image/png We experience any comments and ideas! If you have any comments or ideas, we will continue to optimize Relink.

Downloads last month

Spaces using Laxhar/anime_aesthetic_variant 2