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: 0.7105
Accuracy: 0.7396

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: 0.001
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
1.1123	1.0	376	1.1251	0.3356
0.9352	2.0	752	0.8920	0.5259
0.794	3.0	1128	0.7965	0.5526
0.8608	4.0	1504	0.8108	0.5643
0.8805	5.0	1880	0.7967	0.5543
0.7654	6.0	2256	0.8049	0.5693
0.7409	7.0	2632	0.7866	0.5726
0.7913	8.0	3008	0.7897	0.5893
0.7475	9.0	3384	0.7603	0.6127
0.7457	10.0	3760	0.7604	0.5810
0.6765	11.0	4136	0.7505	0.6060
0.7009	12.0	4512	0.7036	0.6628
0.682	13.0	4888	0.7131	0.6477
0.665	14.0	5264	0.7097	0.6628
0.6075	15.0	5640	0.7283	0.6361
0.6104	16.0	6016	0.7252	0.6845
0.5756	17.0	6392	0.7019	0.6761
0.6308	18.0	6768	0.7144	0.6678
0.6215	19.0	7144	0.6989	0.6644
0.5991	20.0	7520	0.6580	0.7262
0.644	21.0	7896	0.6509	0.7129
0.5561	22.0	8272	0.6219	0.7112
0.5743	23.0	8648	0.6259	0.7095
0.5779	24.0	9024	0.7360	0.6611
0.5703	25.0	9400	0.7402	0.6544
0.59	26.0	9776	0.6505	0.7179
0.4484	27.0	10152	0.7061	0.6945
0.5078	28.0	10528	0.6625	0.7012
0.4947	29.0	10904	0.7197	0.6878
0.4804	30.0	11280	0.6601	0.7129
0.571	31.0	11656	0.6610	0.6978
0.5506	32.0	12032	0.6726	0.7012
0.4066	33.0	12408	0.6633	0.7095
0.4713	34.0	12784	0.6198	0.7245
0.4603	35.0	13160	0.6655	0.7145
0.4936	36.0	13536	0.6620	0.7212
0.4422	37.0	13912	0.6199	0.7446
0.4404	38.0	14288	0.6881	0.7062
0.4643	39.0	14664	0.6209	0.7412
0.4403	40.0	15040	0.6524	0.7496
0.4197	41.0	15416	0.6575	0.7229
0.3846	42.0	15792	0.6496	0.7295
0.3794	43.0	16168	0.6583	0.7179
0.4461	44.0	16544	0.6644	0.7329
0.3616	45.0	16920	0.6911	0.7396
0.3764	46.0	17296	0.7023	0.7279
0.39	47.0	17672	0.6999	0.7379
0.3595	48.0	18048	0.7003	0.7379
0.3678	49.0	18424	0.6974	0.7379
0.2726	50.0	18800	0.7105	0.7396

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_5x_beit_base_rms_001_fold1

smids_5x_beit_base_rms_001_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_5x_beit_base_rms_001_fold1