metadata
library_name: keras
widget:
- text: input
output:
url: ./assets/input.png
- text: target
output:
url: ./assets/target.png
- text: output
output:
url: ./assets/output.png
tags:
- adversarial
- aesthetic
- quality
- filter
metrics:
- TopIQ-FR
- ArcFace Cosine Distance
license: gpl-3.0
base_model:
- vnet
- logasja/ArcFace
pipeline_tag: image-to-image
datasets:
- logasja/FDF
- Prompt
- input
- Prompt
- target
- Prompt
- output
Training logs here
Model Description
This model uses a modified vnet for 2D input/output implemented here with the following configuration.
{
"activation": "ReLU",
"batch_norm": false,
"filter_num": [
64,
128,
256,
512,
512
],
"n_labels": 3,
"output_activation": "tanh",
"pool": false,
"res_num_ini": 1,
"res_num_max": 3,
"unpool": false
}
{
"alpha": 0.0001,
"batch": 64,
"epochs": 500,
"epsilon": 1,
"input": "(256, 256)",
"losses": {
"FEAT_ArcFace": {
"d": "cosine_similarity",
"f": "ArcFace",
"name": "FEAT_ArcFace",
"reduction": "sum_over_batch_size",
"threshold": 0.68,
"weight": 0.1
},
"TopIQ": {
"full_ref": true,
"lower_better": false,
"name": "TopIQ",
"reduction": "sum_over_batch_size",
"score_range": "~0, ~1",
"weight": 0.9
},
"mean_squared_error": {
"name": "mean_squared_error",
"reduction": "sum_over_batch_size",
"weight": 0.1
}
},
"mixed_precision": true,
"optimizer": {
"amsgrad": false,
"beta_1": 0.9,
"beta_2": 0.999,
"clipnorm": null,
"clipvalue": null,
"ema_momentum": 0.99,
"ema_overwrite_frequency": null,
"epsilon": 1e-07,
"global_clipnorm": null,
"gradient_accumulation_steps": null,
"learning_rate": 9.999999747378752e-05,
"loss_scale_factor": null,
"name": "adamw",
"use_ema": false,
"weight_decay": 0.004
},
"seed": "BIIIIIGSTRETCH",
"testing": 0.01,
"training": 0.99
}
Model Architecture Plot
