smids_5x_deit_tiny_sgd_00001_fold3

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

• Loss: 1.0782
• Accuracy: 0.445

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: 1e-05
• 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
1.4131	1.0	375	1.3423	0.3433
1.3593	2.0	750	1.3099	0.3483
1.3082	3.0	1125	1.2818	0.3517
1.3385	4.0	1500	1.2580	0.36
1.2471	5.0	1875	1.2378	0.3633
1.2728	6.0	2250	1.2206	0.3667
1.2244	7.0	2625	1.2061	0.3767
1.1927	8.0	3000	1.1938	0.385
1.1353	9.0	3375	1.1833	0.39
1.1411	10.0	3750	1.1743	0.39
1.1528	11.0	4125	1.1664	0.395
1.1479	12.0	4500	1.1594	0.3917
1.1757	13.0	4875	1.1532	0.3917
1.1667	14.0	5250	1.1477	0.4017
1.1486	15.0	5625	1.1425	0.3967
1.0937	16.0	6000	1.1378	0.4017
1.1232	17.0	6375	1.1333	0.4133
1.1438	18.0	6750	1.1292	0.4183
1.0814	19.0	7125	1.1253	0.42
1.101	20.0	7500	1.1217	0.4183
1.0634	21.0	7875	1.1182	0.42
1.0937	22.0	8250	1.1150	0.4167
1.107	23.0	8625	1.1120	0.4183
1.1086	24.0	9000	1.1091	0.42
1.0802	25.0	9375	1.1064	0.4217
1.1004	26.0	9750	1.1038	0.4233
1.0865	27.0	10125	1.1014	0.4267
1.0686	28.0	10500	1.0991	0.425
1.0719	29.0	10875	1.0969	0.4267
1.0892	30.0	11250	1.0949	0.4267
1.0865	31.0	11625	1.0931	0.4233
1.1008	32.0	12000	1.0913	0.425
1.0834	33.0	12375	1.0897	0.4267
1.085	34.0	12750	1.0882	0.4317
1.0201	35.0	13125	1.0868	0.4367
1.043	36.0	13500	1.0855	0.4367
1.0791	37.0	13875	1.0844	0.4367
1.0443	38.0	14250	1.0833	0.4367
1.0648	39.0	14625	1.0824	0.4383
1.0415	40.0	15000	1.0816	0.4417
1.025	41.0	15375	1.0808	0.4417
1.0078	42.0	15750	1.0802	0.4417
1.0383	43.0	16125	1.0797	0.4433
1.061	44.0	16500	1.0792	0.4433
1.0733	45.0	16875	1.0789	0.4433
1.039	46.0	17250	1.0786	0.4433
1.091	47.0	17625	1.0784	0.445
1.0592	48.0	18000	1.0783	0.445
1.0783	49.0	18375	1.0782	0.445
1.066	50.0	18750	1.0782	0.445

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2