hushem_5x_beit_base_sgd_00001_fold4

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

• Loss: 1.4856
• Accuracy: 0.3095

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
1.5648	1.0	28	1.5024	0.3095
1.5958	2.0	56	1.5016	0.3095
1.5478	3.0	84	1.5008	0.3095
1.6175	4.0	112	1.5001	0.3095
1.5019	5.0	140	1.4994	0.3095
1.5612	6.0	168	1.4987	0.3095
1.5556	7.0	196	1.4981	0.3095
1.5275	8.0	224	1.4974	0.3095
1.529	9.0	252	1.4968	0.3095
1.5306	10.0	280	1.4962	0.3095
1.5486	11.0	308	1.4956	0.3095
1.5567	12.0	336	1.4950	0.3095
1.5578	13.0	364	1.4945	0.3095
1.5601	14.0	392	1.4939	0.3095
1.5869	15.0	420	1.4934	0.3095
1.5292	16.0	448	1.4929	0.3095
1.584	17.0	476	1.4924	0.3095
1.5709	18.0	504	1.4919	0.3095
1.5246	19.0	532	1.4915	0.3095
1.508	20.0	560	1.4911	0.3095
1.5627	21.0	588	1.4907	0.3095
1.543	22.0	616	1.4904	0.3095
1.5306	23.0	644	1.4900	0.3095
1.5347	24.0	672	1.4896	0.3095
1.5296	25.0	700	1.4893	0.3095
1.5722	26.0	728	1.4889	0.3095
1.6103	27.0	756	1.4886	0.3095
1.5352	28.0	784	1.4883	0.3095
1.5133	29.0	812	1.4880	0.3095
1.4677	30.0	840	1.4878	0.3095
1.5424	31.0	868	1.4876	0.3095
1.5132	32.0	896	1.4873	0.3095
1.5611	33.0	924	1.4871	0.3095
1.5494	34.0	952	1.4869	0.3095
1.5087	35.0	980	1.4867	0.3095
1.5719	36.0	1008	1.4865	0.3095
1.5037	37.0	1036	1.4864	0.3095
1.5457	38.0	1064	1.4863	0.3095
1.5227	39.0	1092	1.4861	0.3095
1.5024	40.0	1120	1.4860	0.3095
1.5112	41.0	1148	1.4859	0.3095
1.4872	42.0	1176	1.4858	0.3095
1.5623	43.0	1204	1.4858	0.3095
1.5147	44.0	1232	1.4857	0.3095
1.5196	45.0	1260	1.4857	0.3095
1.5574	46.0	1288	1.4856	0.3095
1.5277	47.0	1316	1.4856	0.3095
1.602	48.0	1344	1.4856	0.3095
1.5259	49.0	1372	1.4856	0.3095
1.5075	50.0	1400	1.4856	0.3095

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0