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README.md
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library_name: keras
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
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---
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library_name: keras
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---
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<Gallery />
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Training logs [here](https://wandb.ai/spuds/auramask/runs/c7f3f8c1813cc1ea9727ea45501e3ce1)
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# Model Description
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This model uses a modified vnet for 2D input/output implemented [here](https://github.com/logasja/keras3-unets) with the following configuration.
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```json
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{
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"activation": "ReLU",
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"batch_norm": false,
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"filter_num": [
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64,
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128,
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256,
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512,
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512
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],
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"n_labels": 3,
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"output_activation": "tanh",
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"pool": false,
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"res_num_ini": 1,
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"res_num_max": 3,
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"unpool": false
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}
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```
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```json
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{
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"alpha": 0.0001,
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"batch": 64,
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"epochs": 500,
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"epsilon": 1,
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"input": "(256, 256)",
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"losses": {
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"FEAT_ArcFace": {
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"d": "cosine_similarity",
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"f": "ArcFace",
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"name": "FEAT_ArcFace",
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"reduction": "sum_over_batch_size",
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"threshold": 0.68,
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"weight": 0.1
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},
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"TopIQ": {
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"full_ref": true,
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"lower_better": false,
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"name": "TopIQ",
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"reduction": "sum_over_batch_size",
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"score_range": "~0, ~1",
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"weight": 0.9
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},
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"mean_squared_error": {
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"name": "mean_squared_error",
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"reduction": "sum_over_batch_size",
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"weight": 0.1
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}
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},
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"mixed_precision": true,
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"optimizer": {
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"amsgrad": false,
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"beta_1": 0.9,
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"beta_2": 0.999,
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"clipnorm": null,
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"clipvalue": null,
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"ema_momentum": 0.99,
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"ema_overwrite_frequency": null,
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"epsilon": 1e-07,
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"global_clipnorm": null,
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"gradient_accumulation_steps": null,
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"learning_rate": 9.999999747378752e-05,
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"loss_scale_factor": null,
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"name": "adamw",
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"use_ema": false,
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"weight_decay": 0.004
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},
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"seed": "BIIIIIGSTRETCH",
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"testing": 0.01,
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"training": 0.99
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}
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```
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## Model Architecture Plot
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
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