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: 0.7848
Accuracy: 0.9183

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
0.2779	1.0	375	0.3054	0.8733
0.2162	2.0	750	0.2359	0.92
0.1285	3.0	1125	0.2539	0.9217
0.0945	4.0	1500	0.2722	0.9233
0.1011	5.0	1875	0.3075	0.92
0.0628	6.0	2250	0.3567	0.9167
0.0288	7.0	2625	0.3944	0.915
0.0403	8.0	3000	0.4745	0.9083
0.0254	9.0	3375	0.4777	0.92
0.0101	10.0	3750	0.5260	0.9233
0.0079	11.0	4125	0.5710	0.92
0.0161	12.0	4500	0.5888	0.915
0.0114	13.0	4875	0.6115	0.92
0.0178	14.0	5250	0.6193	0.915
0.0098	15.0	5625	0.6503	0.9183
0.0165	16.0	6000	0.6581	0.9233
0.0022	17.0	6375	0.6879	0.9217
0.0225	18.0	6750	0.7059	0.92
0.0007	19.0	7125	0.7568	0.9117
0.0104	20.0	7500	0.6995	0.92
0.0014	21.0	7875	0.7129	0.9183
0.0053	22.0	8250	0.7485	0.9133
0.0549	23.0	8625	0.7098	0.9183
0.0039	24.0	9000	0.7046	0.9183
0.0037	25.0	9375	0.7588	0.915
0.0003	26.0	9750	0.7455	0.92
0.0253	27.0	10125	0.8244	0.9033
0.025	28.0	10500	0.7649	0.915
0.0003	29.0	10875	0.7615	0.9183
0.0276	30.0	11250	0.7366	0.92
0.0005	31.0	11625	0.7763	0.915
0.0305	32.0	12000	0.7932	0.91
0.0001	33.0	12375	0.7611	0.9183
0.0308	34.0	12750	0.7888	0.905
0.0002	35.0	13125	0.7612	0.9183
0.0004	36.0	13500	0.7891	0.9167
0.0001	37.0	13875	0.7612	0.9183
0.0	38.0	14250	0.7623	0.9167
0.0009	39.0	14625	0.7611	0.9167
0.0068	40.0	15000	0.7732	0.9167
0.0008	41.0	15375	0.7647	0.92
0.0059	42.0	15750	0.7690	0.915
0.0001	43.0	16125	0.7709	0.92
0.0042	44.0	16500	0.7831	0.9183
0.0002	45.0	16875	0.7842	0.92
0.0105	46.0	17250	0.7861	0.9183
0.0007	47.0	17625	0.7770	0.915
0.0	48.0	18000	0.7805	0.9183
0.0	49.0	18375	0.7842	0.9183
0.0	50.0	18750	0.7848	0.9183

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_3x_beit_base_adamax_00001_fold3

smids_3x_beit_base_adamax_00001_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_3x_beit_base_adamax_00001_fold3