dwiedarioo/vit-base-patch16-224-in21k-datascience8

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: 0.0061
• Train Accuracy: 1.0
• Train Top-3-accuracy: 1.0
• Validation Loss: 0.1289
• Validation Accuracy: 0.9633
• Validation Top-3-accuracy: 0.9935
• Epoch: 53

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
2.2657	0.4219	0.6250	1.9041	0.5875	0.8121	0
1.5469	0.7006	0.8771	1.3444	0.7322	0.9136	1
1.0263	0.8519	0.9553	0.9408	0.8769	0.9719	2
0.6814	0.9412	0.9893	0.6752	0.9244	0.9827	3
0.4663	0.9779	0.9966	0.5106	0.9460	0.9935	4
0.3372	0.9927	0.9981	0.4127	0.9503	0.9892	5
0.2526	0.9958	0.9989	0.3468	0.9546	0.9914	6
0.2015	0.9973	1.0	0.3072	0.9568	0.9914	7
0.1663	0.9981	1.0	0.2609	0.9611	0.9935	8
0.1391	0.9989	0.9996	0.2353	0.9654	0.9957	9
0.1186	0.9992	1.0	0.2889	0.9438	0.9914	10
0.1201	0.9954	0.9996	0.3820	0.9006	0.9762	11
0.1402	0.9905	1.0	0.2185	0.9546	0.9892	12
0.0812	1.0	1.0	0.1898	0.9590	0.9914	13
0.0697	1.0	1.0	0.1757	0.9611	0.9935	14
0.0618	1.0	1.0	0.1698	0.9611	0.9914	15
0.0554	1.0	1.0	0.1625	0.9611	0.9935	16
0.0500	1.0	1.0	0.1592	0.9611	0.9935	17
0.0454	1.0	1.0	0.1526	0.9611	0.9935	18
0.0415	1.0	1.0	0.1494	0.9611	0.9935	19
0.0380	1.0	1.0	0.1473	0.9590	0.9935	20
0.0350	1.0	1.0	0.1443	0.9590	0.9935	21
0.0323	1.0	1.0	0.1403	0.9611	0.9935	22
0.0299	1.0	1.0	0.1408	0.9590	0.9935	23
0.0277	1.0	1.0	0.1368	0.9590	0.9935	24
0.0258	1.0	1.0	0.1369	0.9611	0.9935	25
0.0241	1.0	1.0	0.1361	0.9590	0.9935	26
0.0225	1.0	1.0	0.1355	0.9590	0.9935	27
0.0211	1.0	1.0	0.1349	0.9611	0.9935	28
0.0197	1.0	1.0	0.1312	0.9590	0.9935	29
0.0185	1.0	1.0	0.1317	0.9590	0.9935	30
0.0175	1.0	1.0	0.1328	0.9611	0.9935	31
0.0165	1.0	1.0	0.1318	0.9611	0.9935	32
0.0155	1.0	1.0	0.1320	0.9611	0.9935	33
0.0147	1.0	1.0	0.1294	0.9611	0.9935	34
0.0139	1.0	1.0	0.1306	0.9611	0.9935	35
0.0132	1.0	1.0	0.1291	0.9611	0.9935	36
0.0125	1.0	1.0	0.1295	0.9611	0.9935	37
0.0119	1.0	1.0	0.1306	0.9611	0.9935	38
0.0113	1.0	1.0	0.1275	0.9633	0.9935	39
0.0107	1.0	1.0	0.1282	0.9633	0.9935	40
0.0102	1.0	1.0	0.1272	0.9633	0.9935	41
0.0097	1.0	1.0	0.1282	0.9633	0.9935	42
0.0093	1.0	1.0	0.1269	0.9633	0.9935	43
0.0089	1.0	1.0	0.1286	0.9633	0.9935	44
0.0085	1.0	1.0	0.1278	0.9633	0.9935	45
0.0081	1.0	1.0	0.1285	0.9633	0.9935	46
0.0078	1.0	1.0	0.1291	0.9633	0.9935	47
0.0074	1.0	1.0	0.1290	0.9633	0.9935	48
0.0071	1.0	1.0	0.1283	0.9633	0.9935	49
0.0068	1.0	1.0	0.1292	0.9633	0.9935	50
0.0066	1.0	1.0	0.1295	0.9633	0.9935	51
0.0063	1.0	1.0	0.1290	0.9633	0.9935	52
0.0061	1.0	1.0	0.1289	0.9633	0.9935	53

Framework versions

• Transformers 4.35.2
• TensorFlow 2.14.0
• Tokenizers 0.15.0