smids_10x_deit_small_adamax_001_fold4

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

• Loss: 1.6552
• Accuracy: 0.8733

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
0.2966	1.0	750	0.3694	0.8683
0.2339	2.0	1500	0.4363	0.8417
0.2355	3.0	2250	0.4010	0.8633
0.1515	4.0	3000	0.4419	0.8767
0.2106	5.0	3750	0.4830	0.8683
0.1439	6.0	4500	0.4916	0.8683
0.0791	7.0	5250	0.5811	0.875
0.077	8.0	6000	0.7477	0.8617
0.0484	9.0	6750	0.9268	0.8383
0.0478	10.0	7500	0.8560	0.855
0.0454	11.0	8250	0.7616	0.8733
0.0414	12.0	9000	0.8591	0.8617
0.0224	13.0	9750	0.8231	0.8833
0.0062	14.0	10500	0.9264	0.8717
0.004	15.0	11250	0.8932	0.8783
0.0299	16.0	12000	0.8030	0.8733
0.0268	17.0	12750	0.8616	0.88
0.0071	18.0	13500	0.9511	0.8767
0.0023	19.0	14250	0.9282	0.8783
0.008	20.0	15000	1.1898	0.855
0.0085	21.0	15750	1.0698	0.8683
0.0003	22.0	16500	1.1571	0.8633
0.0004	23.0	17250	1.1256	0.8783
0.0035	24.0	18000	1.2671	0.8633
0.0	25.0	18750	1.1579	0.8683
0.002	26.0	19500	1.2159	0.87
0.0001	27.0	20250	1.2282	0.8717
0.0	28.0	21000	1.2713	0.8683
0.0	29.0	21750	1.3150	0.8683
0.0	30.0	22500	1.2639	0.8733
0.0	31.0	23250	1.4238	0.865
0.0	32.0	24000	1.3138	0.8717
0.0	33.0	24750	1.4236	0.8733
0.0	34.0	25500	1.4930	0.865
0.0	35.0	26250	1.4369	0.87
0.0	36.0	27000	1.4573	0.8667
0.0	37.0	27750	1.4567	0.8717
0.0	38.0	28500	1.4973	0.8767
0.0	39.0	29250	1.5427	0.8667
0.0	40.0	30000	1.5656	0.8717
0.0	41.0	30750	1.5787	0.8717
0.0	42.0	31500	1.5845	0.87
0.0	43.0	32250	1.5904	0.8717
0.0	44.0	33000	1.5995	0.8717
0.0	45.0	33750	1.6192	0.8717
0.0	46.0	34500	1.6307	0.8717
0.0	47.0	35250	1.6406	0.8733
0.0	48.0	36000	1.6477	0.8733
0.0	49.0	36750	1.6529	0.8733
0.0	50.0	37500	1.6552	0.8733

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2