smids_5x_deit_base_sgd_0001_fold5

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

• Loss: 0.5006
• Accuracy: 0.8133

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: 0.0001
• 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
1.0891	1.0	375	1.0922	0.36
1.0632	2.0	750	1.0728	0.4133
1.0234	3.0	1125	1.0519	0.4667
1.0095	4.0	1500	1.0279	0.505
0.9691	5.0	1875	1.0014	0.54
0.9521	6.0	2250	0.9722	0.5683
0.9099	7.0	2625	0.9405	0.6033
0.8832	8.0	3000	0.9085	0.6267
0.8563	9.0	3375	0.8771	0.6533
0.8097	10.0	3750	0.8470	0.685
0.7629	11.0	4125	0.8186	0.705
0.7531	12.0	4500	0.7923	0.715
0.7082	13.0	4875	0.7677	0.7333
0.7318	14.0	5250	0.7449	0.7433
0.7243	15.0	5625	0.7237	0.7533
0.6668	16.0	6000	0.7041	0.7567
0.6939	17.0	6375	0.6860	0.76
0.6736	18.0	6750	0.6692	0.77
0.6795	19.0	7125	0.6538	0.78
0.6094	20.0	7500	0.6398	0.7833
0.5982	21.0	7875	0.6269	0.7817
0.5784	22.0	8250	0.6150	0.7867
0.6034	23.0	8625	0.6042	0.7933
0.6235	24.0	9000	0.5942	0.7967
0.5888	25.0	9375	0.5851	0.7933
0.5892	26.0	9750	0.5766	0.7933
0.5908	27.0	10125	0.5688	0.7983
0.5781	28.0	10500	0.5616	0.7983
0.5631	29.0	10875	0.5551	0.8
0.5055	30.0	11250	0.5492	0.8017
0.5168	31.0	11625	0.5436	0.805
0.5659	32.0	12000	0.5386	0.81
0.568	33.0	12375	0.5339	0.8083
0.5472	34.0	12750	0.5295	0.8117
0.5227	35.0	13125	0.5256	0.81
0.4679	36.0	13500	0.5220	0.81
0.5236	37.0	13875	0.5188	0.8117
0.5206	38.0	14250	0.5158	0.8117
0.5047	39.0	14625	0.5132	0.8133
0.5461	40.0	15000	0.5108	0.8133
0.495	41.0	15375	0.5087	0.8133
0.508	42.0	15750	0.5069	0.8133
0.5153	43.0	16125	0.5053	0.8133
0.4846	44.0	16500	0.5040	0.8133
0.5055	45.0	16875	0.5029	0.8133
0.5156	46.0	17250	0.5020	0.8133
0.525	47.0	17625	0.5013	0.8133
0.4795	48.0	18000	0.5009	0.8133
0.4888	49.0	18375	0.5006	0.8133
0.4989	50.0	18750	0.5006	0.8133

Framework versions

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