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: 0.2634
Accuracy: 0.8965

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: 0.001
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
0.5921	1.0	751	0.6125	0.7713
0.3591	2.0	1502	0.4405	0.8314
0.3047	3.0	2253	0.3783	0.8598
0.2551	4.0	3004	0.3472	0.8781
0.2903	5.0	3755	0.3280	0.8748
0.2606	6.0	4506	0.3152	0.8765
0.2793	7.0	5257	0.3058	0.8881
0.2466	8.0	6008	0.2984	0.8915
0.2613	9.0	6759	0.2938	0.8881
0.2309	10.0	7510	0.2878	0.8948
0.2362	11.0	8261	0.2856	0.8915
0.2432	12.0	9012	0.2826	0.8982
0.2244	13.0	9763	0.2806	0.8898
0.1581	14.0	10514	0.2786	0.8915
0.1869	15.0	11265	0.2757	0.8915
0.1863	16.0	12016	0.2739	0.8915
0.25	17.0	12767	0.2738	0.8898
0.1609	18.0	13518	0.2717	0.8932
0.1993	19.0	14269	0.2719	0.8881
0.1982	20.0	15020	0.2698	0.8915
0.1558	21.0	15771	0.2692	0.8982
0.1601	22.0	16522	0.2690	0.8998
0.1754	23.0	17273	0.2694	0.8898
0.1664	24.0	18024	0.2677	0.8932
0.1901	25.0	18775	0.2669	0.8948
0.2023	26.0	19526	0.2671	0.8965
0.1982	27.0	20277	0.2651	0.8965
0.2093	28.0	21028	0.2655	0.8998
0.1635	29.0	21779	0.2645	0.8982
0.1441	30.0	22530	0.2639	0.8998
0.1066	31.0	23281	0.2648	0.9015
0.227	32.0	24032	0.2644	0.9032
0.1777	33.0	24783	0.2648	0.8965
0.1846	34.0	25534	0.2641	0.8982
0.1694	35.0	26285	0.2643	0.8982
0.1711	36.0	27036	0.2639	0.8965
0.2649	37.0	27787	0.2640	0.9032
0.1541	38.0	28538	0.2641	0.8982
0.107	39.0	29289	0.2640	0.8932
0.2208	40.0	30040	0.2641	0.8998
0.1601	41.0	30791	0.2638	0.8948
0.1237	42.0	31542	0.2635	0.8965
0.1676	43.0	32293	0.2637	0.8932
0.1349	44.0	33044	0.2638	0.8965
0.1329	45.0	33795	0.2638	0.8965
0.177	46.0	34546	0.2636	0.8982
0.1141	47.0	35297	0.2635	0.8965
0.1694	48.0	36048	0.2636	0.8965
0.1549	49.0	36799	0.2634	0.8965
0.1223	50.0	37550	0.2634	0.8965

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_10x_deit_base_sgd_001_fold1

smids_10x_deit_base_sgd_001_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_10x_deit_base_sgd_001_fold1