smids_3x_deit_small_adamax_001_fold5

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.9259
• Accuracy: 0.8867

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:

• learning_rate: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5128	1.0	225	0.3960	0.845
0.2912	2.0	450	0.3545	0.855
0.2355	3.0	675	0.5138	0.8283
0.2322	4.0	900	0.3502	0.8567
0.1566	5.0	1125	0.4327	0.855
0.1654	6.0	1350	0.3396	0.8883
0.1711	7.0	1575	0.4306	0.8733
0.1936	8.0	1800	0.4649	0.8783
0.1252	9.0	2025	0.4662	0.875
0.0897	10.0	2250	0.4869	0.8767
0.0556	11.0	2475	0.4677	0.8883
0.117	12.0	2700	0.5865	0.8667
0.1159	13.0	2925	0.6366	0.87
0.0526	14.0	3150	0.5638	0.8917
0.0199	15.0	3375	0.6092	0.885
0.0105	16.0	3600	0.7275	0.8817
0.0173	17.0	3825	0.6827	0.8883
0.0471	18.0	4050	0.6845	0.87
0.0272	19.0	4275	0.6814	0.8933
0.0245	20.0	4500	0.7971	0.8767
0.0042	21.0	4725	0.7308	0.8867
0.0003	22.0	4950	0.8304	0.895
0.0001	23.0	5175	0.8296	0.8917
0.0235	24.0	5400	0.7781	0.88
0.0005	25.0	5625	0.8114	0.8833
0.0112	26.0	5850	0.8185	0.8833
0.0	27.0	6075	0.9165	0.8833
0.0034	28.0	6300	0.9111	0.8783
0.0037	29.0	6525	0.8969	0.8833
0.0001	30.0	6750	0.9635	0.8783
0.0	31.0	6975	0.8389	0.8933
0.0	32.0	7200	0.9477	0.8767
0.003	33.0	7425	0.8612	0.8917
0.0001	34.0	7650	0.8689	0.8983
0.0025	35.0	7875	0.8114	0.885
0.0	36.0	8100	0.8426	0.8867
0.0037	37.0	8325	0.8625	0.8833
0.0006	38.0	8550	0.8725	0.885
0.0	39.0	8775	0.8789	0.8833
0.0	40.0	9000	0.8405	0.885
0.0	41.0	9225	0.8737	0.8833
0.0	42.0	9450	0.9022	0.8867
0.003	43.0	9675	0.9115	0.8833
0.0	44.0	9900	0.9099	0.8867
0.0	45.0	10125	0.9104	0.8883
0.0	46.0	10350	0.9175	0.8867
0.0	47.0	10575	0.9200	0.8867
0.0	48.0	10800	0.9224	0.8867
0.0	49.0	11025	0.9241	0.8867
0.0	50.0	11250	0.9259	0.8867

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2