logasja/FDF
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How to use WillNuyen228/auramask-sketchv1 with Keras:
# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
model = keras.saving.load_model("hf://WillNuyen228/auramask-sketchv1")



Training logs here
This model uses a modified vnet for 2D input/output implemented here with the following configuration.
{
"D": [
1,
1,
1,
1,
1
],
"E": [
1,
1,
2,
3,
5
],
"activation": "relu",
"batch_norm": false,
"filter_num": [
16,
32,
64,
128,
128
],
"kernel_reg": "l2",
"n_labels": 3,
"output_activation": null
}
{
"alpha": 0.0001,
"batch": 16,
"epochs": 50,
"epsilon": 0.03,
"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": false,
"optimizer": {
"amsgrad": false,
"beta_1": 0.9,
"beta_2": 0.999,
"clipnorm": 1,
"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": "adam",
"use_ema": false,
"weight_decay": null
},
"seed": "LKUHKVHVALZF",
"testing": 0.1,
"training": 0.9
}
Base model
logasja/ArcFace