This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.2586
Accuracy: 0.55

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	40	1.8677	0.3688
No log	2.0	80	1.5622	0.3625
No log	3.0	120	1.4344	0.5375
No log	4.0	160	1.2909	0.5
No log	5.0	200	1.2146	0.6
No log	6.0	240	1.2457	0.55
No log	7.0	280	1.2429	0.5563
No log	8.0	320	1.2015	0.5375
No log	9.0	360	1.2393	0.5188
No log	10.0	400	1.1908	0.5687
No log	11.0	440	1.1580	0.6188
No log	12.0	480	1.1608	0.575
1.0532	13.0	520	1.2468	0.5687
1.0532	14.0	560	1.2747	0.5188
1.0532	15.0	600	1.3293	0.525
1.0532	16.0	640	1.3720	0.525
1.0532	17.0	680	1.4374	0.5125
1.0532	18.0	720	1.3092	0.5687
1.0532	19.0	760	1.4143	0.5437
1.0532	20.0	800	1.5023	0.4938
1.0532	21.0	840	1.4033	0.575
1.0532	22.0	880	1.4476	0.5437
1.0532	23.0	920	1.3089	0.5813
1.0532	24.0	960	1.3866	0.5813
0.3016	25.0	1000	1.3748	0.5875
0.3016	26.0	1040	1.5846	0.5312
0.3016	27.0	1080	1.3451	0.5875
0.3016	28.0	1120	1.5289	0.5062
0.3016	29.0	1160	1.6067	0.5125
0.3016	30.0	1200	1.5002	0.5375
0.3016	31.0	1240	1.5404	0.55
0.3016	32.0	1280	1.5542	0.5563
0.3016	33.0	1320	1.4320	0.6062
0.3016	34.0	1360	1.6465	0.5312
0.3016	35.0	1400	1.7259	0.5062
0.3016	36.0	1440	1.5655	0.5687
0.3016	37.0	1480	1.4517	0.6188
0.1764	38.0	1520	1.5884	0.575
0.1764	39.0	1560	1.4692	0.5813
0.1764	40.0	1600	1.5062	0.6125
0.1764	41.0	1640	1.5122	0.6
0.1764	42.0	1680	1.5859	0.6
0.1764	43.0	1720	1.6816	0.525
0.1764	44.0	1760	1.5594	0.6062
0.1764	45.0	1800	1.7011	0.5375
0.1764	46.0	1840	1.5676	0.575
0.1764	47.0	1880	1.5260	0.6
0.1764	48.0	1920	1.5711	0.575
0.1764	49.0	1960	1.7095	0.5563
0.1256	50.0	2000	1.7625	0.5188

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

saskiadwiulfah1810/image_classification

image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

saskiadwiulfah1810/
image_classification