vit-base-patch16-224-in21k-final2multibrainmri

dwiedarioo/vit-base-patch16-224-in21k-final2multibrainmri

vit-base-patch16-224-in21k-final2multibrainmri is a image classification model from dwiedarioo. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Nov 11, 2023
Updated: Nov 12, 2023
SHA: 031647fc

Base models

google/vit-base-patch16-224-in21k

Model card

From the Hugging Face model README

dwiedarioo/vit-base-patch16-224-in21k-final2multibrainmri

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: 0.0072
Train Accuracy: 1.0
Train Top-3-accuracy: 1.0
Validation Loss: 0.1111
Validation Accuracy: 0.9719
Validation Top-3-accuracy: 0.9914
Epoch: 49

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
2.2742	0.3856	0.6522	1.8596	0.6112	0.8337	0
1.5673	0.6919	0.8778	1.3120	0.7883	0.9136	1
1.0377	0.8622	0.9576	0.9078	0.8661	0.9611	2
0.6816	0.9511	0.9859	0.6497	0.9222	0.9849	3
0.4698	0.9805	0.9939	0.5104	0.9395	0.9870	4
0.3375	0.9897	0.9973	0.3975	0.9590	0.9892	5
0.2554	0.9966	0.9992	0.3107	0.9676	0.9978	6
0.2346	0.9905	0.9992	0.3804	0.9287	0.9914	7
0.1976	0.9935	0.9989	0.3250	0.9546	0.9914	8
0.1686	0.9939	0.9992	0.4980	0.8920	0.9762	9
0.1423	0.9969	0.9996	0.2129	0.9654	0.9957	10
0.1073	0.9992	1.0	0.1840	0.9741	0.9978	11
0.0925	0.9992	1.0	0.1714	0.9719	0.9978	12
0.0809	0.9992	1.0	0.1595	0.9719	0.9978	13
0.0715	0.9992	1.0	0.1503	0.9719	0.9978	14
0.0637	1.0	1.0	0.1426	0.9762	0.9978	15
0.0573	0.9996	1.0	0.1361	0.9784	0.9978	16
0.0516	1.0	1.0	0.1325	0.9784	0.9957	17
0.0469	1.0	1.0	0.1279	0.9784	0.9957	18
0.0427	1.0	1.0	0.1248	0.9784	0.9957	19
0.0392	1.0	1.0	0.1224	0.9784	0.9957	20
0.0359	1.0	1.0	0.1191	0.9784	0.9957	21
0.0331	1.0	1.0	0.1178	0.9762	0.9914	22
0.0306	1.0	1.0	0.1162	0.9784	0.9957	23
0.0284	1.0	1.0	0.1144	0.9784	0.9957	24
0.0264	1.0	1.0	0.1143	0.9741	0.9957	25
0.0246	1.0	1.0	0.1126	0.9762	0.9957	26
0.0230	1.0	1.0	0.1104	0.9784	0.9957	27
0.0215	1.0	1.0	0.1110	0.9762	0.9935	28
0.0201	1.0	1.0	0.1091	0.9762	0.9957	29
0.0189	1.0	1.0	0.1101	0.9741	0.9957	30
0.0178	1.0	1.0	0.1099	0.9762	0.9914	31
0.0167	1.0	1.0	0.1091	0.9762	0.9935	32
0.0158	1.0	1.0	0.1091	0.9762	0.9914	33
0.0149	1.0	1.0	0.1094	0.9741	0.9914	34
0.0141	1.0	1.0	0.1088	0.9719	0.9914	35
0.0134	1.0	1.0	0.1089	0.9762	0.9914	36
0.0127	1.0	1.0	0.1084	0.9741	0.9935	37
0.0120	1.0	1.0	0.1087	0.9741	0.9914	38
0.0114	1.0	1.0	0.1078	0.9741	0.9914	39
0.0109	1.0	1.0	0.1088	0.9719	0.9914	40
0.0104	1.0	1.0	0.1087	0.9719	0.9914	41
0.0099	1.0	1.0	0.1094	0.9719	0.9935	42
0.0094	1.0	1.0	0.1095	0.9719	0.9914	43
0.0090	1.0	1.0	0.1099	0.9719	0.9914	44
0.0086	1.0	1.0	0.1112	0.9719	0.9914	45
0.0082	1.0	1.0	0.1104	0.9719	0.9914	46
0.0079	1.0	1.0	0.1107	0.9719	0.9914	47
0.0075	1.0	1.0	0.1102	0.9741	0.9914	48
0.0072	1.0	1.0	0.1111	0.9719	0.9914	49

Framework versions

Transformers 4.35.0
TensorFlow 2.14.0
Datasets 2.14.6
Tokenizers 0.14.1

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Nov 11, 2023
Updated: Nov 12, 2023