hushem_5x_beit_base_sgd_0001_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: 1.3804
• Accuracy: 0.4048

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
1.4632	1.0	28	1.5173	0.2143
1.3741	2.0	56	1.5094	0.2381
1.4021	3.0	84	1.5010	0.2381
1.3681	4.0	112	1.4942	0.2381
1.4122	5.0	140	1.4872	0.2381
1.3657	6.0	168	1.4803	0.2619
1.3993	7.0	196	1.4742	0.2619
1.3652	8.0	224	1.4681	0.2619
1.3615	9.0	252	1.4624	0.2619
1.3492	10.0	280	1.4574	0.2619
1.3205	11.0	308	1.4526	0.2619
1.3552	12.0	336	1.4476	0.2619
1.3393	13.0	364	1.4435	0.2619
1.3397	14.0	392	1.4389	0.2619
1.3561	15.0	420	1.4347	0.2619
1.3361	16.0	448	1.4313	0.2619
1.3287	17.0	476	1.4281	0.2857
1.3138	18.0	504	1.4246	0.3095
1.3241	19.0	532	1.4213	0.3095
1.3033	20.0	560	1.4184	0.3095
1.3163	21.0	588	1.4155	0.3095
1.3116	22.0	616	1.4126	0.3095
1.3228	23.0	644	1.4101	0.3095
1.3214	24.0	672	1.4076	0.3333
1.2818	25.0	700	1.4051	0.3333
1.2948	26.0	728	1.4029	0.3333
1.3231	27.0	756	1.4008	0.3333
1.2969	28.0	784	1.3988	0.3333
1.2659	29.0	812	1.3969	0.3333
1.2426	30.0	840	1.3952	0.3571
1.2934	31.0	868	1.3935	0.3810
1.2777	32.0	896	1.3917	0.4048
1.2767	33.0	924	1.3904	0.4048
1.3162	34.0	952	1.3892	0.4048
1.2726	35.0	980	1.3880	0.4048
1.294	36.0	1008	1.3868	0.4048
1.2554	37.0	1036	1.3858	0.4048
1.2838	38.0	1064	1.3848	0.4048
1.2842	39.0	1092	1.3839	0.4048
1.2721	40.0	1120	1.3832	0.4048
1.2562	41.0	1148	1.3826	0.4048
1.2576	42.0	1176	1.3821	0.4048
1.3	43.0	1204	1.3815	0.4048
1.273	44.0	1232	1.3811	0.4048
1.2913	45.0	1260	1.3808	0.4048
1.2814	46.0	1288	1.3806	0.4048
1.2272	47.0	1316	1.3805	0.4048
1.2516	48.0	1344	1.3804	0.4048
1.2555	49.0	1372	1.3804	0.4048
1.3084	50.0	1400	1.3804	0.4048

Framework versions

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