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dwiedarioo/
vit-base-patch16-224-in21k-finalmultibrainmri

Name: vit-base-patch16-224-in21k-finalmultibrainmri
Availability: InStock

generated_from_keras_callback

google/vit-base-patch16-224-in21k

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dwiedarioo/vit-base-patch16-224-in21k-finalmultibrainmri

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.1240
Train Accuracy: 0.9989
Train Top-3-accuracy: 1.0
Validation Loss: 0.2638
Validation Accuracy: 0.9568
Validation Top-3-accuracy: 0.9892
Epoch: 10

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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Train Top-3-accuracy	Validation Loss	Validation Accuracy	Validation Top-3-accuracy	Epoch
2.2501	0.3937	0.6346	1.8763	0.5551	0.8035	0
1.5448	0.6808	0.8732	1.3666	0.7127	0.8812	1
1.0471	0.8324	0.9439	0.9732	0.8402	0.9568	2
0.7074	0.9385	0.9828	0.7078	0.9266	0.9849	3
0.4854	0.9748	0.9924	0.5190	0.9374	0.9892	4
0.3465	0.9905	0.9962	0.4126	0.9482	0.9935	5
0.2571	0.9950	0.9981	0.3267	0.9719	0.9957	6
0.2031	0.9962	0.9992	0.2788	0.9741	0.9957	7
0.1667	0.9985	1.0	0.2484	0.9698	0.9957	8
0.1398	0.9992	1.0	0.2225	0.9719	0.9957	9
0.1240	0.9989	1.0	0.2638	0.9568	0.9892	10

Framework versions

Transformers 4.35.0
TensorFlow 2.14.0
Datasets 2.14.6
Tokenizers 0.14.1