smids_10x_beit_large_adamax_001_fold5

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: 0.8836
• Accuracy: 0.905

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
0.3665	1.0	750	0.3594	0.8583
0.2964	2.0	1500	0.4126	0.8483
0.2817	3.0	2250	0.2955	0.895
0.2107	4.0	3000	0.4285	0.8483
0.2441	5.0	3750	0.2917	0.905
0.2284	6.0	4500	0.3000	0.8933
0.1417	7.0	5250	0.3775	0.9033
0.1212	8.0	6000	0.4010	0.9
0.1114	9.0	6750	0.3900	0.8917
0.1229	10.0	7500	0.5863	0.8833
0.0978	11.0	8250	0.5114	0.8883
0.019	12.0	9000	0.6596	0.9033
0.0244	13.0	9750	0.6428	0.9017
0.0242	14.0	10500	0.6293	0.9
0.0159	15.0	11250	0.5943	0.9067
0.0287	16.0	12000	0.4876	0.9033
0.0161	17.0	12750	0.7094	0.8933
0.0033	18.0	13500	0.7392	0.9117
0.0133	19.0	14250	0.6855	0.9017
0.0009	20.0	15000	0.7025	0.895
0.033	21.0	15750	0.5767	0.895
0.0007	22.0	16500	0.6533	0.8983
0.0005	23.0	17250	0.8501	0.8883
0.0041	24.0	18000	0.6751	0.91
0.0016	25.0	18750	0.8175	0.8983
0.022	26.0	19500	0.7166	0.9067
0.002	27.0	20250	0.7746	0.9033
0.0002	28.0	21000	0.7048	0.91
0.0002	29.0	21750	0.8217	0.9083
0.0187	30.0	22500	0.7107	0.8983
0.0002	31.0	23250	0.7863	0.9133
0.0	32.0	24000	0.8314	0.8983
0.0	33.0	24750	0.7909	0.8967
0.0003	34.0	25500	0.8566	0.905
0.0	35.0	26250	0.7280	0.9117
0.0	36.0	27000	0.8236	0.9017
0.0068	37.0	27750	0.7886	0.92
0.0	38.0	28500	0.8302	0.9017
0.0	39.0	29250	0.8589	0.9067
0.0	40.0	30000	0.8152	0.9017
0.0	41.0	30750	0.8501	0.905
0.0	42.0	31500	0.8563	0.91
0.0	43.0	32250	0.7690	0.9117
0.0	44.0	33000	0.8007	0.9083
0.0	45.0	33750	0.8622	0.9033
0.0001	46.0	34500	0.8624	0.905
0.0	47.0	35250	0.8665	0.9067
0.0	48.0	36000	0.8739	0.9067
0.0	49.0	36750	0.8825	0.9067
0.0	50.0	37500	0.8836	0.905

Framework versions

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