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: 0.6879
Accuracy: 0.9

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
0.2538	1.0	375	0.3339	0.8467
0.1892	2.0	750	0.2698	0.8917
0.1183	3.0	1125	0.2626	0.9
0.1141	4.0	1500	0.2917	0.8883
0.071	5.0	1875	0.3136	0.9
0.0217	6.0	2250	0.3618	0.8933
0.0346	7.0	2625	0.4213	0.89
0.0047	8.0	3000	0.4689	0.8983
0.022	9.0	3375	0.5039	0.8983
0.0024	10.0	3750	0.5358	0.8917
0.0059	11.0	4125	0.5640	0.895
0.0001	12.0	4500	0.5647	0.8967
0.0001	13.0	4875	0.6088	0.895
0.0002	14.0	5250	0.5907	0.9017
0.0001	15.0	5625	0.6332	0.8967
0.0001	16.0	6000	0.6424	0.8833
0.0001	17.0	6375	0.6207	0.8983
0.0068	18.0	6750	0.6552	0.895
0.0	19.0	7125	0.6642	0.9017
0.0	20.0	7500	0.6453	0.8883
0.0001	21.0	7875	0.6986	0.895
0.0265	22.0	8250	0.7065	0.8883
0.0	23.0	8625	0.6670	0.8967
0.0	24.0	9000	0.6793	0.8967
0.0	25.0	9375	0.6516	0.9017
0.0	26.0	9750	0.6626	0.89
0.0	27.0	10125	0.6877	0.895
0.0	28.0	10500	0.6598	0.8967
0.0	29.0	10875	0.6682	0.8933
0.0145	30.0	11250	0.6761	0.8983
0.0	31.0	11625	0.6763	0.8983
0.0	32.0	12000	0.6749	0.8967
0.0	33.0	12375	0.6798	0.8983
0.0	34.0	12750	0.6830	0.8967
0.0	35.0	13125	0.6787	0.9
0.0	36.0	13500	0.6883	0.895
0.0	37.0	13875	0.6805	0.8983
0.003	38.0	14250	0.6825	0.895
0.0	39.0	14625	0.6851	0.8967
0.0	40.0	15000	0.6877	0.8983
0.0	41.0	15375	0.6804	0.8983
0.0	42.0	15750	0.6888	0.8983
0.0	43.0	16125	0.6877	0.8983
0.0	44.0	16500	0.6899	0.8983
0.0	45.0	16875	0.6911	0.8967
0.0	46.0	17250	0.6870	0.8983
0.0023	47.0	17625	0.6868	0.8983
0.0	48.0	18000	0.6892	0.9
0.0	49.0	18375	0.6892	0.9
0.0021	50.0	18750	0.6879	0.9

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_5x_deit_base_adamax_00001_fold5

smids_5x_deit_base_adamax_00001_fold5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_5x_deit_base_adamax_00001_fold5