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

Loss: 1.1898
Accuracy: 0.8833

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
0.2529	1.0	750	0.3083	0.8717
0.1742	2.0	1500	0.3045	0.8817
0.085	3.0	2250	0.3224	0.8917
0.0584	4.0	3000	0.3946	0.89
0.0231	5.0	3750	0.5149	0.8783
0.0068	6.0	4500	0.5629	0.8917
0.0006	7.0	5250	0.6401	0.8883
0.0003	8.0	6000	0.7450	0.8833
0.0003	9.0	6750	0.7717	0.8867
0.0001	10.0	7500	0.8308	0.8817
0.0	11.0	8250	0.8547	0.8867
0.0001	12.0	9000	0.8939	0.8817
0.0	13.0	9750	0.9205	0.8783
0.0	14.0	10500	0.9505	0.8783
0.0	15.0	11250	0.9627	0.885
0.0277	16.0	12000	0.9874	0.88
0.0	17.0	12750	0.9840	0.8783
0.0	18.0	13500	1.0047	0.885
0.0	19.0	14250	1.0103	0.8833
0.0	20.0	15000	1.0263	0.88
0.0	21.0	15750	1.0370	0.8867
0.0	22.0	16500	1.0526	0.885
0.0	23.0	17250	1.0655	0.8867
0.0	24.0	18000	1.0468	0.8917
0.0	25.0	18750	1.0862	0.88
0.0	26.0	19500	1.1000	0.8833
0.0	27.0	20250	1.0936	0.885
0.0	28.0	21000	1.1024	0.885
0.0	29.0	21750	1.1116	0.885
0.0	30.0	22500	1.1299	0.88
0.0	31.0	23250	1.1217	0.8833
0.0	32.0	24000	1.1546	0.8817
0.0	33.0	24750	1.1366	0.8833
0.0	34.0	25500	1.1374	0.8833
0.0	35.0	26250	1.1519	0.8833
0.0	36.0	27000	1.1620	0.8833
0.0	37.0	27750	1.1614	0.8833
0.0	38.0	28500	1.1586	0.8833
0.0	39.0	29250	1.1778	0.8833
0.0	40.0	30000	1.1771	0.8833
0.0	41.0	30750	1.1795	0.8833
0.0	42.0	31500	1.1811	0.8833
0.0	43.0	32250	1.1780	0.885
0.0	44.0	33000	1.1793	0.885
0.0	45.0	33750	1.1843	0.8833
0.0	46.0	34500	1.1866	0.8833
0.0	47.0	35250	1.1877	0.8833
0.0	48.0	36000	1.1886	0.8833
0.0	49.0	36750	1.1894	0.8833
0.0	50.0	37500	1.1898	0.8833

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_10x_deit_base_adamax_00001_fold4

smids_10x_deit_base_adamax_00001_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_10x_deit_base_adamax_00001_fold4