aditnnda/felidae_klasifikasi_fix

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

• Train Loss: 0.2941
• Train Accuracy: 0.9180
• Validation Loss: 0.2691
• Validation Accuracy: 0.9180
• 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: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 9100, '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
1.5677	0.6885	1.4799	0.6885	0
1.4926	0.9180	1.3584	0.9180	1
1.3957	0.9016	1.2332	0.9016	2
1.3151	0.8361	1.1098	0.8361	3
1.1756	0.8525	0.9886	0.8525	4
1.1173	0.8852	0.8775	0.8852	5
1.0196	0.9016	0.8042	0.9016	6
0.9451	0.9344	0.6991	0.9344	7
0.9189	0.9508	0.6396	0.9508	8
0.8269	0.9672	0.5717	0.9672	9
0.7581	0.9836	0.5063	0.9836	10
0.7328	0.9672	0.4772	0.9672	11
0.6639	0.9508	0.4691	0.9508	12
0.7082	0.9180	0.4597	0.9180	13
0.6607	0.9672	0.4006	0.9672	14
0.7278	0.9508	0.3966	0.9508	15
0.6285	0.9508	0.3669	0.9508	16
0.5902	0.9836	0.3321	0.9836	17
0.5963	0.9344	0.3899	0.9344	18
0.6273	0.9344	0.3246	0.9344	19
0.5917	0.9016	0.4248	0.9016	20
0.5190	0.9180	0.3575	0.9180	21
0.4991	0.9508	0.3060	0.9508	22
0.4861	0.9344	0.3244	0.9344	23
0.4650	0.9344	0.3014	0.9344	24
0.5044	0.9508	0.2727	0.9508	25
0.4232	0.9672	0.2439	0.9672	26
0.4247	0.9672	0.2233	0.9672	27
0.4732	0.9508	0.2857	0.9508	28
0.4222	1.0	0.1937	1.0	29
0.4165	0.9836	0.1996	0.9836	30
0.3979	0.9508	0.2074	0.9508	31
0.3691	0.9344	0.2627	0.9344	32
0.3094	0.9344	0.2641	0.9344	33
0.3726	0.9508	0.2224	0.9508	34
0.3503	0.9508	0.2241	0.9508	35
0.3832	0.9344	0.2274	0.9344	36
0.3765	0.9508	0.2421	0.9508	37
0.3927	0.9508	0.1979	0.9508	38
0.2968	0.9672	0.1857	0.9672	39
0.3489	0.9508	0.2158	0.9508	40
0.4102	0.9672	0.1951	0.9672	41
0.3842	0.9672	0.1971	0.9672	42
0.3417	0.8852	0.3686	0.8852	43
0.3219	0.9344	0.2255	0.9344	44
0.3671	0.9672	0.1570	0.9672	45
0.3948	0.9344	0.2217	0.9344	46
0.3201	0.9672	0.1993	0.9672	47
0.3612	0.9508	0.1936	0.9508	48
0.2941	0.9180	0.2691	0.9180	49

Framework versions

• Transformers 4.35.1
• TensorFlow 2.14.0
• Datasets 2.14.6
• Tokenizers 0.14.1