README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_keras_callback
model-index:
  - name: eyesCare_firstTryEntrnal_mix_model-1
    results: []

eyesCare_firstTryEntrnal_mix_model-1

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: 0.0066
• Train Accuracy: 0.8616
• Train Top-3-accuracy: 0.9785
• Validation Loss: 1.9942
• Validation Accuracy: 0.8627
• Validation Top-3-accuracy: 0.9787
• 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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 4950, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Train Top-3-accuracy	Validation Loss	Validation Accuracy	Validation Top-3-accuracy	Epoch
1.3981	0.3217	0.7428	1.1812	0.4135	0.8283	0
1.1137	0.4540	0.8600	1.0974	0.4763	0.8802	1
0.9296	0.5034	0.8955	1.0739	0.5231	0.9065	2
0.7444	0.5473	0.9155	1.1126	0.5663	0.9225	3
0.5534	0.5880	0.9285	1.1673	0.6076	0.9342	4
0.4105	0.6261	0.9387	1.1547	0.6422	0.9428	5
0.2830	0.6586	0.9462	1.3119	0.6729	0.9493	6
0.1984	0.6874	0.9519	1.3821	0.6990	0.9540	7
0.1224	0.7104	0.9559	1.4778	0.7213	0.9576	8
0.1021	0.7313	0.9591	1.5426	0.7400	0.9603	9
0.1017	0.7478	0.9615	1.6387	0.7545	0.9625	10
0.0646	0.7613	0.9635	1.6226	0.7678	0.9644	11
0.0500	0.7738	0.9654	1.6646	0.7793	0.9662	12
0.0571	0.7843	0.9669	1.7492	0.7890	0.9675	13
0.0248	0.7935	0.9682	1.6984	0.7978	0.9689	14
0.0185	0.8020	0.9695	1.7302	0.8059	0.9701	15
0.0145	0.8096	0.9707	1.7669	0.8129	0.9712	16
0.0129	0.8163	0.9718	1.7972	0.8193	0.9722	17
0.0116	0.8223	0.9727	1.8276	0.8251	0.9732	18
0.0106	0.8277	0.9736	1.8544	0.8302	0.9739	19
0.0098	0.8326	0.9743	1.8792	0.8348	0.9746	20
0.0091	0.8370	0.9749	1.9012	0.8391	0.9752	21
0.0085	0.8411	0.9755	1.9212	0.8430	0.9758	22
0.0080	0.8448	0.9761	1.9391	0.8465	0.9763	23
0.0076	0.8482	0.9766	1.9547	0.8498	0.9768	24
0.0073	0.8513	0.9770	1.9682	0.8527	0.9772	25
0.0070	0.8542	0.9774	1.9789	0.8555	0.9777	26
0.0068	0.8568	0.9778	1.9871	0.8580	0.9780	27
0.0067	0.8593	0.9782	1.9924	0.8605	0.9784	28
0.0066	0.8616	0.9785	1.9942	0.8627	0.9787	29

Framework versions

• Transformers 4.44.2
• TensorFlow 2.15.0
• Datasets 2.21.0
• Tokenizers 0.19.1