smids_10x_beit_large_adamax_001_fold3

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

• Loss: 1.0324
• Accuracy: 0.9017

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
0.3438	1.0	750	0.3826	0.8517
0.2931	2.0	1500	0.3034	0.89
0.2025	3.0	2250	0.3971	0.8783
0.2582	4.0	3000	0.3086	0.8867
0.2483	5.0	3750	0.3346	0.8917
0.1606	6.0	4500	0.3908	0.8717
0.1236	7.0	5250	0.4286	0.8783
0.1197	8.0	6000	0.3887	0.9
0.0412	9.0	6750	0.4924	0.885
0.0384	10.0	7500	0.5551	0.89
0.0583	11.0	8250	0.4882	0.9017
0.0806	12.0	9000	0.5902	0.88
0.0489	13.0	9750	0.5212	0.88
0.0353	14.0	10500	0.5171	0.9
0.0094	15.0	11250	0.6341	0.895
0.0154	16.0	12000	0.5409	0.9133
0.0118	17.0	12750	0.6110	0.8833
0.0159	18.0	13500	0.6873	0.9033
0.0026	19.0	14250	0.7871	0.8983
0.0163	20.0	15000	0.6341	0.895
0.0002	21.0	15750	0.7139	0.9017
0.0006	22.0	16500	0.6717	0.9033
0.0266	23.0	17250	0.6268	0.895
0.0051	24.0	18000	0.6425	0.905
0.0	25.0	18750	0.7506	0.91
0.0004	26.0	19500	0.6864	0.9017
0.0002	27.0	20250	0.6111	0.9117
0.0163	28.0	21000	0.6875	0.9017
0.0001	29.0	21750	0.8050	0.8967
0.0002	30.0	22500	0.7397	0.8967
0.0004	31.0	23250	0.8218	0.8983
0.0	32.0	24000	0.8725	0.8983
0.0	33.0	24750	0.9662	0.8967
0.0	34.0	25500	0.9148	0.9083
0.0	35.0	26250	0.8492	0.9083
0.0001	36.0	27000	0.8264	0.9067
0.0	37.0	27750	0.8650	0.895
0.0004	38.0	28500	0.9030	0.91
0.0	39.0	29250	0.9540	0.9
0.0	40.0	30000	1.0292	0.8883
0.0	41.0	30750	1.0282	0.8917
0.0	42.0	31500	1.0128	0.8933
0.0	43.0	32250	1.0147	0.8983
0.0	44.0	33000	0.9709	0.8983
0.0	45.0	33750	0.9643	0.9067
0.0	46.0	34500	0.9770	0.9017
0.0	47.0	35250	1.0000	0.8983
0.0	48.0	36000	1.0223	0.9017
0.0	49.0	36750	1.0291	0.9017
0.0	50.0	37500	1.0324	0.9017

Framework versions

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