hushem_5x_beit_base_sgd_0001_fold3

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: 1.4647
• Accuracy: 0.2791

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
1.5793	1.0	28	1.5789	0.2558
1.5183	2.0	56	1.5712	0.2558
1.5213	3.0	84	1.5641	0.2558
1.4605	4.0	112	1.5574	0.2558
1.4855	5.0	140	1.5511	0.2558
1.4714	6.0	168	1.5448	0.2558
1.5489	7.0	196	1.5392	0.2791
1.4903	8.0	224	1.5342	0.2791
1.4325	9.0	252	1.5290	0.2791
1.4353	10.0	280	1.5246	0.2558
1.4693	11.0	308	1.5207	0.2558
1.4343	12.0	336	1.5162	0.2558
1.4713	13.0	364	1.5122	0.2558
1.4732	14.0	392	1.5085	0.2558
1.517	15.0	420	1.5050	0.2558
1.4521	16.0	448	1.5018	0.2558
1.4309	17.0	476	1.4988	0.2558
1.4246	18.0	504	1.4964	0.2558
1.4231	19.0	532	1.4937	0.2558
1.4691	20.0	560	1.4912	0.2558
1.4305	21.0	588	1.4888	0.2558
1.4575	22.0	616	1.4865	0.2558
1.4268	23.0	644	1.4845	0.2558
1.3904	24.0	672	1.4827	0.2558
1.4432	25.0	700	1.4808	0.2558
1.4078	26.0	728	1.4793	0.2558
1.382	27.0	756	1.4777	0.2558
1.3894	28.0	784	1.4764	0.2558
1.4046	29.0	812	1.4751	0.2558
1.4273	30.0	840	1.4741	0.2791
1.3786	31.0	868	1.4730	0.2791
1.3777	32.0	896	1.4719	0.2791
1.3887	33.0	924	1.4708	0.2791
1.3651	34.0	952	1.4700	0.2791
1.4904	35.0	980	1.4692	0.2791
1.3288	36.0	1008	1.4686	0.2791
1.3653	37.0	1036	1.4680	0.2791
1.3833	38.0	1064	1.4673	0.2791
1.3973	39.0	1092	1.4668	0.2791
1.4044	40.0	1120	1.4663	0.2791
1.3896	41.0	1148	1.4659	0.2791
1.3676	42.0	1176	1.4656	0.2791
1.3444	43.0	1204	1.4654	0.2791
1.3782	44.0	1232	1.4651	0.2791
1.44	45.0	1260	1.4650	0.2791
1.383	46.0	1288	1.4648	0.2791
1.3752	47.0	1316	1.4648	0.2791
1.343	48.0	1344	1.4647	0.2791
1.3923	49.0	1372	1.4647	0.2791
1.429	50.0	1400	1.4647	0.2791

Framework versions

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