smids_10x_deit_small_sgd_0001_fold4

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: 0.4063
• Accuracy: 0.8417

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
0.9715	1.0	750	1.0172	0.455
0.9076	2.0	1500	0.9524	0.5267
0.8403	3.0	2250	0.8812	0.625
0.7987	4.0	3000	0.8125	0.6817
0.7256	5.0	3750	0.7521	0.7183
0.6364	6.0	4500	0.7018	0.7483
0.5752	7.0	5250	0.6571	0.775
0.63	8.0	6000	0.6211	0.7817
0.6197	9.0	6750	0.5901	0.79
0.5118	10.0	7500	0.5651	0.7983
0.5006	11.0	8250	0.5449	0.8017
0.5617	12.0	9000	0.5276	0.8033
0.4842	13.0	9750	0.5134	0.8083
0.5031	14.0	10500	0.5016	0.81
0.4417	15.0	11250	0.4908	0.8083
0.4457	16.0	12000	0.4818	0.8083
0.3768	17.0	12750	0.4743	0.8117
0.4232	18.0	13500	0.4671	0.8167
0.4491	19.0	14250	0.4614	0.8167
0.4472	20.0	15000	0.4557	0.8233
0.3954	21.0	15750	0.4506	0.8267
0.405	22.0	16500	0.4463	0.83
0.4169	23.0	17250	0.4425	0.8317
0.4563	24.0	18000	0.4389	0.8333
0.3987	25.0	18750	0.4356	0.8333
0.39	26.0	19500	0.4325	0.8317
0.4056	27.0	20250	0.4297	0.8317
0.3872	28.0	21000	0.4272	0.8317
0.3817	29.0	21750	0.4249	0.835
0.4035	30.0	22500	0.4229	0.8367
0.3636	31.0	23250	0.4211	0.835
0.4122	32.0	24000	0.4193	0.8367
0.3917	33.0	24750	0.4176	0.8383
0.3839	34.0	25500	0.4161	0.84
0.3217	35.0	26250	0.4147	0.84
0.3641	36.0	27000	0.4136	0.84
0.3379	37.0	27750	0.4124	0.84
0.3959	38.0	28500	0.4115	0.84
0.3972	39.0	29250	0.4106	0.84
0.3899	40.0	30000	0.4098	0.84
0.3662	41.0	30750	0.4090	0.84
0.3473	42.0	31500	0.4084	0.8417
0.3905	43.0	32250	0.4078	0.8417
0.3794	44.0	33000	0.4074	0.8417
0.3783	45.0	33750	0.4070	0.8417
0.3309	46.0	34500	0.4067	0.8417
0.3086	47.0	35250	0.4065	0.8417
0.3454	48.0	36000	0.4063	0.8417
0.3559	49.0	36750	0.4063	0.8417
0.323	50.0	37500	0.4063	0.8417

Framework versions

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