hushem_40x_deit_tiny_adamax_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: 0.5391
• Accuracy: 0.9524

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
0.2113	1.0	219	0.5669	0.8571
0.1247	2.0	438	0.3852	0.8571
0.0797	3.0	657	0.3243	0.8810
0.079	4.0	876	0.2935	0.9286
0.1755	5.0	1095	0.3153	0.8810
0.1228	6.0	1314	0.4983	0.9048
0.047	7.0	1533	0.4737	0.9048
0.0236	8.0	1752	0.2530	0.9286
0.0027	9.0	1971	0.9366	0.8810
0.0257	10.0	2190	0.8815	0.8810
0.032	11.0	2409	0.7642	0.9048
0.0025	12.0	2628	0.6321	0.9286
0.0	13.0	2847	0.4805	0.9048
0.0406	14.0	3066	0.7911	0.9286
0.0286	15.0	3285	0.2463	0.9048
0.0029	16.0	3504	0.0537	0.9762
0.0065	17.0	3723	0.3008	0.9286
0.0001	18.0	3942	0.8021	0.8810
0.0	19.0	4161	0.3160	0.9762
0.0084	20.0	4380	1.2037	0.8333
0.0	21.0	4599	0.5426	0.9286
0.0001	22.0	4818	0.3468	0.9524
0.0204	23.0	5037	0.7324	0.9286
0.0	24.0	5256	0.8099	0.9048
0.0	25.0	5475	1.1998	0.8810
0.0	26.0	5694	0.5294	0.9524
0.0	27.0	5913	0.5383	0.9524
0.0	28.0	6132	0.5204	0.9524
0.0	29.0	6351	0.5193	0.9524
0.0	30.0	6570	0.5189	0.9524
0.0	31.0	6789	0.5187	0.9524
0.0	32.0	7008	0.5190	0.9524
0.0	33.0	7227	0.5187	0.9524
0.0	34.0	7446	0.5193	0.9524
0.0	35.0	7665	0.5201	0.9524
0.0	36.0	7884	0.5213	0.9524
0.0	37.0	8103	0.5225	0.9524
0.0	38.0	8322	0.5239	0.9524
0.0	39.0	8541	0.5256	0.9524
0.0	40.0	8760	0.5271	0.9524
0.0	41.0	8979	0.5287	0.9524
0.0	42.0	9198	0.5302	0.9524
0.0	43.0	9417	0.5318	0.9524
0.0	44.0	9636	0.5333	0.9524
0.0	45.0	9855	0.5348	0.9524
0.0	46.0	10074	0.5359	0.9524
0.0	47.0	10293	0.5372	0.9524
0.0	48.0	10512	0.5381	0.9524
0.0	49.0	10731	0.5389	0.9524
0.0	50.0	10950	0.5391	0.9524

Framework versions

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