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beit-base-patch16-224-dmae-va-U5-42

This model is a fine-tuned version of microsoft/beit-base-patch16-224 on Augusto777/dmae-ve-U5 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0039
  • Accuracy: 0.8167

Model description

Model for multiclass detection of age-related macular degeneration.

Intended uses & limitations

Destined to support medical diagnosis.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 42

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9 7 1.3471 0.4667
1.6023 1.94 15 1.0873 0.5833
1.1509 2.97 23 0.9948 0.5833
0.826 4.0 31 0.7244 0.7167
0.826 4.9 38 0.5741 0.7333
0.5551 5.94 46 0.6569 0.75
0.3649 6.97 54 0.6322 0.7167
0.2592 8.0 62 0.6994 0.7333
0.2592 8.9 69 0.6590 0.7333
0.1958 9.94 77 0.6846 0.7667
0.1664 10.97 85 0.7166 0.7667
0.1571 12.0 93 0.7842 0.7833
0.1174 12.9 100 0.8465 0.8
0.1174 13.94 108 0.9116 0.7667
0.0956 14.97 116 0.9741 0.75
0.1252 16.0 124 0.7760 0.8
0.0933 16.9 131 0.9424 0.7833
0.0933 17.94 139 1.0445 0.7333
0.1455 18.97 147 0.8525 0.7333
0.1034 20.0 155 0.8222 0.7667
0.0855 20.9 162 0.8991 0.7833
0.0985 21.94 170 0.8955 0.8
0.0985 22.97 178 0.9603 0.7667
0.087 24.0 186 0.9932 0.7833
0.0832 24.9 193 1.0100 0.7833
0.0632 25.94 201 0.9393 0.7667
0.0632 26.97 209 0.9062 0.7833
0.0778 28.0 217 0.9339 0.8
0.0627 28.9 224 1.0039 0.8167
0.0837 29.94 232 1.0636 0.7333
0.0595 30.97 240 1.0424 0.75
0.0595 32.0 248 1.0514 0.8
0.0706 32.9 255 1.0639 0.7833
0.0565 33.94 263 1.0494 0.7667
0.0515 34.97 271 1.0628 0.7667
0.0515 36.0 279 1.1089 0.7667
0.0614 36.9 286 1.0861 0.8
0.0496 37.94 294 1.0713 0.8

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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