hafizurUMaine/cifar10_m

This model is a fine-tuned version of apple/mobilevit-xx-small on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0748
• Train Accuracy: 0.9743
• Validation Loss: 0.6597
• Validation Accuracy: 0.8575
• 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': 400000, '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
5.5748	0.1482	3.1655	0.4160	0
2.4468	0.5135	1.7772	0.6195	1
1.5927	0.6389	1.3152	0.6770	2
1.2333	0.7001	1.1226	0.7265	3
1.0094	0.7334	0.9668	0.7490	4
0.8748	0.7591	0.9140	0.7510	5
0.7714	0.7846	0.7881	0.7845	6
0.6977	0.7999	0.8075	0.7745	7
0.6524	0.8096	0.8417	0.7675	8
0.5904	0.8254	0.7763	0.7850	9
0.5525	0.8321	0.7367	0.7955	10
0.5083	0.8459	0.7343	0.7990	11
0.4695	0.8559	0.6768	0.8075	12
0.4432	0.8615	0.6830	0.8095	13
0.4125	0.8704	0.6891	0.7980	14
0.3995	0.875	0.6482	0.8155	15
0.3723	0.8781	0.6653	0.8095	16
0.3505	0.8859	0.6268	0.8195	17
0.3390	0.8906	0.6243	0.8205	18
0.3132	0.8967	0.6338	0.8255	19
0.2879	0.9071	0.5879	0.8380	20
0.2845	0.9066	0.6004	0.8320	21
0.2578	0.9141	0.6228	0.8320	22
0.2521	0.9178	0.6208	0.8295	23
0.2375	0.9258	0.6051	0.8410	24
0.2226	0.9243	0.6138	0.8395	25
0.2139	0.9298	0.5651	0.8455	26
0.2094	0.9302	0.5881	0.8470	27
0.1925	0.9385	0.6298	0.8390	28
0.1806	0.9399	0.5982	0.8450	29
0.1758	0.9401	0.6139	0.8435	30
0.1630	0.9449	0.6105	0.8430	31
0.1566	0.9449	0.5953	0.8490	32
0.1423	0.9531	0.6246	0.8440	33
0.1378	0.9545	0.6249	0.8500	34
0.1379	0.9553	0.6625	0.8415	35
0.1305	0.9551	0.6035	0.8575	36
0.1253	0.9581	0.6503	0.8490	37
0.1149	0.9607	0.5882	0.8585	38
0.1026	0.9672	0.6130	0.8530	39
0.1019	0.9660	0.6373	0.8525	40
0.1038	0.9645	0.6197	0.8570	41
0.0938	0.9685	0.6239	0.8545	42
0.0910	0.9688	0.6439	0.8590	43
0.0869	0.9711	0.5812	0.8640	44
0.0818	0.9726	0.6692	0.8565	45
0.0695	0.9799	0.6652	0.8585	46
0.0756	0.9765	0.6584	0.8570	47
0.0669	0.9797	0.6542	0.8610	48
0.0748	0.9743	0.6597	0.8575	49

Framework versions

• Transformers 4.37.2
• TensorFlow 2.15.0
• Datasets 2.16.1
• Tokenizers 0.15.1