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Prahas10
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shingles

Image Classification
Transformers
TensorFlow
vit
generated_from_keras_callback
Inference Endpoints
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Prahas10
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
  - generated_from_keras_callback
model-index:
  - name: Prahas10/shingles
    results: []

Prahas10/shingles

This model is a fine-tuned version of google/vit-base-patch16-384 on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0993
  • Validation Loss: 0.6967
  • Train Accuracy: 0.8166
  • Epoch: 29

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 4e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 127899.75, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 10370.25, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.0001}
  • training_precision: float32

Training results

Train Loss Validation Loss Train Accuracy Epoch
5.2368 5.2154 0.0047 0
5.1655 5.1337 0.0113 1
5.0415 4.9860 0.0278 2
4.8179 4.7812 0.0781 3
4.4541 4.4703 0.1844 4
3.9330 4.0779 0.2841 5
3.3155 3.6691 0.3650 6
2.6546 3.3371 0.4313 7
2.0435 3.0037 0.4727 8
1.5258 2.7059 0.5193 9
1.1079 2.4174 0.5588 10
0.7989 2.3590 0.5532 11
0.5857 1.9721 0.6298 12
0.4337 1.7442 0.6896 13
0.3352 1.7334 0.6580 14
0.2641 1.6197 0.6670 15
0.2042 1.7021 0.6289 16
0.1642 1.3843 0.7070 17
0.1500 1.4422 0.6787 18
0.1251 1.2797 0.7098 19
0.1093 0.9233 0.8020 20
0.1215 0.9209 0.7977 21
0.1007 0.9143 0.7803 22
0.0811 0.7952 0.8090 23
0.0953 0.7678 0.8260 24
0.1033 0.8928 0.7705 25
0.0636 0.3480 0.9271 26
0.0880 0.5916 0.8669 27
0.0861 0.8892 0.7789 28
0.0993 0.6967 0.8166 29

Framework versions

  • Transformers 4.41.0
  • TensorFlow 2.15.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1