hushem_5x_beit_base_sgd_00001_fold1

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: 1.5922
• Accuracy: 0.2667

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
1.4867	1.0	27	1.6071	0.2667
1.5392	2.0	54	1.6064	0.2667
1.5844	3.0	81	1.6056	0.2667
1.5797	4.0	108	1.6050	0.2667
1.5108	5.0	135	1.6044	0.2667
1.5236	6.0	162	1.6037	0.2667
1.5199	7.0	189	1.6031	0.2667
1.544	8.0	216	1.6026	0.2667
1.5317	9.0	243	1.6020	0.2667
1.537	10.0	270	1.6014	0.2667
1.5415	11.0	297	1.6010	0.2667
1.5478	12.0	324	1.6004	0.2667
1.4666	13.0	351	1.6000	0.2667
1.5352	14.0	378	1.5995	0.2667
1.478	15.0	405	1.5990	0.2667
1.5333	16.0	432	1.5986	0.2667
1.5245	17.0	459	1.5982	0.2667
1.5379	18.0	486	1.5978	0.2667
1.52	19.0	513	1.5975	0.2667
1.5508	20.0	540	1.5971	0.2667
1.5421	21.0	567	1.5967	0.2667
1.4919	22.0	594	1.5963	0.2667
1.483	23.0	621	1.5960	0.2667
1.5087	24.0	648	1.5957	0.2667
1.5236	25.0	675	1.5954	0.2667
1.5228	26.0	702	1.5951	0.2667
1.5439	27.0	729	1.5949	0.2667
1.5272	28.0	756	1.5946	0.2667
1.5029	29.0	783	1.5943	0.2667
1.5695	30.0	810	1.5941	0.2667
1.5057	31.0	837	1.5939	0.2667
1.5092	32.0	864	1.5937	0.2667
1.575	33.0	891	1.5935	0.2667
1.5175	34.0	918	1.5934	0.2667
1.4801	35.0	945	1.5932	0.2667
1.4771	36.0	972	1.5930	0.2667
1.5042	37.0	999	1.5929	0.2667
1.5372	38.0	1026	1.5928	0.2667
1.5158	39.0	1053	1.5927	0.2667
1.4902	40.0	1080	1.5926	0.2667
1.4904	41.0	1107	1.5925	0.2667
1.4817	42.0	1134	1.5924	0.2667
1.5064	43.0	1161	1.5923	0.2667
1.4625	44.0	1188	1.5923	0.2667
1.5064	45.0	1215	1.5923	0.2667
1.4956	46.0	1242	1.5922	0.2667
1.502	47.0	1269	1.5922	0.2667
1.495	48.0	1296	1.5922	0.2667
1.4896	49.0	1323	1.5922	0.2667
1.5118	50.0	1350	1.5922	0.2667

Framework versions

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