This model is a fine-tuned version of google/siglip2-base-patch16-512, specialized for predicting image aesthetics in anime illustrations based on Q-Align's quality metrics.

It achieves the following performance on the evaluation set:

Loss: 0.0225
Mse: 0.0225
Rmse: 0.1502

NOTE: the Q-align model was not well-suited for anime aesthetics, and more or less have a "western" feel to it. The distilled version of Q-Align quality score may be slightly preferrable for actual usages.

(See: trojblue/distill-q-align-quality-siglip2-base)

Model Description

This model is a distilled version of the original Q-Align model, using Siglip2-base as its backbone. It is designed for simpler, faster, and more practical deployment compared to the original model. Benefits include:

Easier integration (no special transformer version required).
Smaller model size and efficient batching.
Produces results very close to the original Q-Align model.

Intended Uses & Limitations

This model is designed specifically for Image Quality Analysis (IQA) tasks on anime illustrations.

It distinguishes image quality from aesthetics, making it suitable for image filtering, recommendations, and curation tasks where quality assessment is essential.

Training and Evaluation Data

The model was trained using approximately 5.8 million anime illustrations sourced equally from Danbooru and Twitter. The training procedure involved:

Generating aesthetic predictions using the original Q-Align model.
Training the Siglip2-base model to mimic these predictions.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 320
eval_batch_size: 256
seed: 1337
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse
0.0405	0.1634	2400	0.0348	0.0348	0.1867
0.0371	0.3268	4800	0.0297	0.0297	0.1724
0.0369	0.4902	7200	0.0311	0.0311	0.1764
0.0315	0.6536	9600	0.0237	0.0237	0.1540
0.033	0.8170	12000	0.0228	0.0228	0.1510
0.0303	0.9805	14400	0.0224	0.0224	0.1498

Framework versions

Transformers 4.49.0
Pytorch 2.1.0+cu121
Datasets 3.3.2
Tokenizers 0.21.0