msislam123/cifar10

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: 1.4844
• Train Accuracy: 0.5160
• Validation Loss: 1.8361
• Validation Accuracy: 0.3676
• Epoch: 19

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': 3e-05, 'decay_steps': 59840, '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': 0.01}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
2.7038	0.1213	2.5039	0.1698	0
2.4263	0.1935	2.3429	0.2179	1
2.2970	0.2343	2.1942	0.2901	2
2.2132	0.2694	2.1083	0.3115	3
2.1136	0.2998	2.0528	0.3102	4
2.0533	0.3145	2.0046	0.3182	5
2.0016	0.3292	1.9495	0.3356	6
1.9511	0.3463	1.9589	0.3182	7
1.9106	0.3636	1.9360	0.3249	8
1.8807	0.3700	1.9207	0.3396	9
1.8368	0.3790	1.8890	0.3556	10
1.8118	0.3951	1.8834	0.3489	11
1.7714	0.3967	1.8410	0.3730	12
1.7185	0.4225	1.8576	0.3396	13
1.6796	0.4439	1.8087	0.3743	14
1.6593	0.4519	1.8192	0.3543	15
1.6208	0.4539	1.8129	0.3650	16
1.5826	0.4826	1.8316	0.3663	17
1.5399	0.4913	1.7991	0.3650	18
1.4844	0.5160	1.8361	0.3676	19

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2