hushem_40x_deit_tiny_sgd_001_fold4

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

• Loss: 0.2388
• Accuracy: 0.8810

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
1.2231	1.0	219	1.3846	0.2857
0.9485	2.0	438	1.1776	0.5238
0.8421	3.0	657	0.9985	0.6429
0.6802	4.0	876	0.8236	0.7381
0.5815	5.0	1095	0.6866	0.7857
0.5091	6.0	1314	0.5853	0.8095
0.3792	7.0	1533	0.5105	0.8333
0.3552	8.0	1752	0.4443	0.8333
0.3174	9.0	1971	0.4029	0.8810
0.2621	10.0	2190	0.3730	0.8571
0.2168	11.0	2409	0.3473	0.8571
0.2263	12.0	2628	0.3296	0.9048
0.1689	13.0	2847	0.3233	0.9048
0.171	14.0	3066	0.3040	0.8810
0.1176	15.0	3285	0.3059	0.8810
0.1241	16.0	3504	0.2811	0.8571
0.1343	17.0	3723	0.2712	0.8571
0.0953	18.0	3942	0.2802	0.8571
0.0918	19.0	4161	0.2700	0.8571
0.0691	20.0	4380	0.2755	0.8571
0.088	21.0	4599	0.2615	0.8571
0.0857	22.0	4818	0.2483	0.8571
0.0654	23.0	5037	0.2562	0.8571
0.0661	24.0	5256	0.2789	0.8571
0.0463	25.0	5475	0.2435	0.8571
0.0362	26.0	5694	0.2633	0.8571
0.0272	27.0	5913	0.2844	0.8571
0.041	28.0	6132	0.2942	0.8571
0.034	29.0	6351	0.2744	0.8571
0.0352	30.0	6570	0.2644	0.8810
0.0212	31.0	6789	0.2648	0.8810
0.0359	32.0	7008	0.2431	0.8810
0.0203	33.0	7227	0.2434	0.8810
0.0209	34.0	7446	0.2577	0.8810
0.0254	35.0	7665	0.2645	0.8810
0.0178	36.0	7884	0.2497	0.8810
0.0232	37.0	8103	0.2639	0.8810
0.015	38.0	8322	0.2391	0.8810
0.0246	39.0	8541	0.2615	0.8810
0.0228	40.0	8760	0.2445	0.8810
0.0203	41.0	8979	0.2448	0.8810
0.014	42.0	9198	0.2402	0.8810
0.0231	43.0	9417	0.2372	0.8810
0.0179	44.0	9636	0.2499	0.8810
0.0197	45.0	9855	0.2540	0.8810
0.0118	46.0	10074	0.2416	0.8810
0.0134	47.0	10293	0.2401	0.8810
0.0155	48.0	10512	0.2412	0.8810
0.0103	49.0	10731	0.2400	0.8810
0.0156	50.0	10950	0.2388	0.8810

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2