smids_10x_deit_tiny_adamax_00001_fold3

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: 0.8454
• Accuracy: 0.9117

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
0.3334	1.0	750	0.3137	0.8867
0.2212	2.0	1500	0.2847	0.8933
0.1557	3.0	2250	0.2655	0.9017
0.1618	4.0	3000	0.2547	0.9083
0.1458	5.0	3750	0.2827	0.9117
0.0827	6.0	4500	0.3021	0.9067
0.0585	7.0	5250	0.3302	0.915
0.038	8.0	6000	0.3826	0.9117
0.0072	9.0	6750	0.4421	0.9033
0.0278	10.0	7500	0.4604	0.9117
0.024	11.0	8250	0.5200	0.9083
0.0385	12.0	9000	0.5950	0.905
0.0036	13.0	9750	0.6062	0.9167
0.0266	14.0	10500	0.6478	0.9083
0.0002	15.0	11250	0.6832	0.905
0.0008	16.0	12000	0.6882	0.9083
0.0001	17.0	12750	0.7251	0.9033
0.0001	18.0	13500	0.7412	0.9
0.0	19.0	14250	0.7303	0.905
0.0	20.0	15000	0.7516	0.9083
0.0	21.0	15750	0.7572	0.905
0.0	22.0	16500	0.7738	0.9033
0.0	23.0	17250	0.7783	0.905
0.0	24.0	18000	0.7887	0.9083
0.0	25.0	18750	0.8047	0.9067
0.0	26.0	19500	0.7947	0.905
0.0	27.0	20250	0.8074	0.9017
0.0	28.0	21000	0.8188	0.9067
0.0	29.0	21750	0.8171	0.9083
0.0	30.0	22500	0.8254	0.91
0.0	31.0	23250	0.8136	0.91
0.0	32.0	24000	0.8161	0.9117
0.0	33.0	24750	0.8510	0.9083
0.0	34.0	25500	0.8503	0.905
0.0	35.0	26250	0.8125	0.9083
0.0	36.0	27000	0.8406	0.9083
0.0	37.0	27750	0.8651	0.9033
0.0	38.0	28500	0.8378	0.9083
0.0	39.0	29250	0.8214	0.905
0.0	40.0	30000	0.8342	0.91
0.0	41.0	30750	0.8303	0.905
0.0	42.0	31500	0.8436	0.9117
0.0	43.0	32250	0.8482	0.9083
0.0	44.0	33000	0.8466	0.9117
0.0	45.0	33750	0.8396	0.9083
0.0	46.0	34500	0.8440	0.9117
0.0	47.0	35250	0.8439	0.9117
0.0	48.0	36000	0.8441	0.91
0.0	49.0	36750	0.8457	0.9117
0.0	50.0	37500	0.8454	0.9117

Framework versions

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