smids_5x_deit_base_adamax_00001_fold1

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: 0.5548
• Accuracy: 0.9115

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
0.2913	1.0	376	0.3175	0.8815
0.197	2.0	752	0.2822	0.8865
0.1857	3.0	1128	0.2733	0.8915
0.0801	4.0	1504	0.2769	0.8965
0.062	5.0	1880	0.2932	0.9048
0.0559	6.0	2256	0.3199	0.9115
0.0331	7.0	2632	0.3568	0.9098
0.0024	8.0	3008	0.4098	0.8982
0.0164	9.0	3384	0.4359	0.9065
0.0004	10.0	3760	0.4455	0.9082
0.0127	11.0	4136	0.4881	0.9082
0.0001	12.0	4512	0.4919	0.9032
0.0001	13.0	4888	0.4968	0.9115
0.0037	14.0	5264	0.5278	0.9065
0.0001	15.0	5640	0.5316	0.9115
0.0001	16.0	6016	0.5363	0.9032
0.0001	17.0	6392	0.5212	0.9149
0.0001	18.0	6768	0.5353	0.9098
0.0	19.0	7144	0.5265	0.9098
0.0147	20.0	7520	0.5277	0.9115
0.0	21.0	7896	0.5565	0.9065
0.0	22.0	8272	0.5728	0.9098
0.0	23.0	8648	0.5461	0.9115
0.0	24.0	9024	0.5300	0.9065
0.0	25.0	9400	0.5373	0.9065
0.0042	26.0	9776	0.5315	0.9082
0.0	27.0	10152	0.5779	0.9065
0.0	28.0	10528	0.5457	0.9098
0.0079	29.0	10904	0.5511	0.9098
0.003	30.0	11280	0.5454	0.9048
0.0	31.0	11656	0.5479	0.9098
0.0	32.0	12032	0.5371	0.9082
0.0	33.0	12408	0.5701	0.9065
0.0	34.0	12784	0.5431	0.9032
0.0	35.0	13160	0.5470	0.9048
0.0	36.0	13536	0.5461	0.9015
0.0	37.0	13912	0.5481	0.9115
0.0	38.0	14288	0.5522	0.9098
0.0	39.0	14664	0.5539	0.9082
0.0	40.0	15040	0.5537	0.9115
0.0	41.0	15416	0.5471	0.9048
0.0	42.0	15792	0.5483	0.9115
0.0	43.0	16168	0.5497	0.9132
0.0	44.0	16544	0.5527	0.9115
0.0	45.0	16920	0.5532	0.9115
0.0053	46.0	17296	0.5512	0.9098
0.0	47.0	17672	0.5538	0.9115
0.0	48.0	18048	0.5539	0.9098
0.0	49.0	18424	0.5540	0.9115
0.0012	50.0	18800	0.5548	0.9115

Framework versions

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