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: 0.8741
Accuracy: 0.8902

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
0.216	1.0	750	0.2934	0.8852
0.1089	2.0	1500	0.3003	0.8885
0.0954	3.0	2250	0.3369	0.8852
0.0518	4.0	3000	0.4029	0.8902
0.0792	5.0	3750	0.4723	0.8819
0.0261	6.0	4500	0.5460	0.8885
0.0158	7.0	5250	0.6663	0.8819
0.0057	8.0	6000	0.6585	0.8802
0.0002	9.0	6750	0.6982	0.8918
0.0006	10.0	7500	0.7734	0.8918
0.0	11.0	8250	0.7967	0.8852
0.0	12.0	9000	0.8312	0.8885
0.0001	13.0	9750	0.7897	0.8869
0.0	14.0	10500	0.8305	0.8885
0.0	15.0	11250	0.7993	0.8869
0.0	16.0	12000	0.8582	0.8835
0.0	17.0	12750	0.8331	0.8902
0.0	18.0	13500	0.8238	0.8952
0.0	19.0	14250	0.8345	0.8952
0.0	20.0	15000	0.8413	0.8885
0.0	21.0	15750	0.8573	0.8918
0.0085	22.0	16500	0.8363	0.8918
0.005	23.0	17250	0.8431	0.8885
0.0	24.0	18000	0.8537	0.8902
0.0	25.0	18750	0.8479	0.8918
0.0	26.0	19500	0.8197	0.8869
0.0098	27.0	20250	0.8247	0.8869
0.0	28.0	21000	0.8273	0.8885
0.0	29.0	21750	0.8481	0.8918
0.0	30.0	22500	0.8486	0.8885
0.0	31.0	23250	0.8913	0.8935
0.0	32.0	24000	0.8769	0.8918
0.0	33.0	24750	0.8699	0.8885
0.0	34.0	25500	0.8861	0.8935
0.0	35.0	26250	0.8555	0.8852
0.0	36.0	27000	0.8657	0.8918
0.0099	37.0	27750	0.8602	0.8902
0.0	38.0	28500	0.8913	0.8918
0.0	39.0	29250	0.8649	0.8885
0.0	40.0	30000	0.8620	0.8885
0.0	41.0	30750	0.8685	0.8885
0.0	42.0	31500	0.8731	0.8902
0.0	43.0	32250	0.8772	0.8902
0.0	44.0	33000	0.8742	0.8902
0.0026	45.0	33750	0.8773	0.8902
0.0	46.0	34500	0.8745	0.8902
0.0	47.0	35250	0.8728	0.8885
0.0	48.0	36000	0.8716	0.8885
0.0	49.0	36750	0.8740	0.8885
0.0	50.0	37500	0.8741	0.8902

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_fold2

smids_10x_deit_base_adamax_00001_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_10x_deit_base_adamax_00001_fold2