smids_10x_beit_large_sgd_00001_fold2

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: 0.7989
• Accuracy: 0.6473

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
1.1712	1.0	750	1.2053	0.3594
1.1095	2.0	1500	1.1744	0.3677
1.079	3.0	2250	1.1476	0.3677
1.0868	4.0	3000	1.1238	0.3760
1.0188	5.0	3750	1.1026	0.4043
1.0313	6.0	4500	1.0830	0.4176
0.9867	7.0	5250	1.0650	0.4343
0.9922	8.0	6000	1.0482	0.4509
1.0089	9.0	6750	1.0324	0.4626
0.9248	10.0	7500	1.0176	0.4809
0.9924	11.0	8250	1.0037	0.4942
0.9341	12.0	9000	0.9905	0.5042
0.9032	13.0	9750	0.9777	0.5158
0.9223	14.0	10500	0.9658	0.5241
0.8875	15.0	11250	0.9546	0.5275
0.8812	16.0	12000	0.9440	0.5408
0.8383	17.0	12750	0.9339	0.5524
0.8368	18.0	13500	0.9242	0.5557
0.8681	19.0	14250	0.9150	0.5657
0.8552	20.0	15000	0.9065	0.5674
0.8564	21.0	15750	0.8983	0.5691
0.8254	22.0	16500	0.8905	0.5740
0.842	23.0	17250	0.8831	0.5807
0.802	24.0	18000	0.8761	0.5857
0.8617	25.0	18750	0.8694	0.5973
0.8384	26.0	19500	0.8631	0.6057
0.8257	27.0	20250	0.8572	0.6106
0.8327	28.0	21000	0.8516	0.6156
0.8111	29.0	21750	0.8464	0.6173
0.7892	30.0	22500	0.8414	0.6206
0.7974	31.0	23250	0.8368	0.6256
0.8791	32.0	24000	0.8325	0.6256
0.7583	33.0	24750	0.8285	0.6306
0.7714	34.0	25500	0.8248	0.6323
0.7891	35.0	26250	0.8214	0.6356
0.7659	36.0	27000	0.8182	0.6389
0.8096	37.0	27750	0.8154	0.6356
0.7644	38.0	28500	0.8128	0.6373
0.8029	39.0	29250	0.8104	0.6406
0.7912	40.0	30000	0.8082	0.6406
0.7766	41.0	30750	0.8063	0.6423
0.7693	42.0	31500	0.8047	0.6439
0.735	43.0	32250	0.8032	0.6456
0.7637	44.0	33000	0.8020	0.6456
0.7733	45.0	33750	0.8010	0.6473
0.7268	46.0	34500	0.8002	0.6473
0.8097	47.0	35250	0.7996	0.6473
0.7648	48.0	36000	0.7991	0.6473
0.7593	49.0	36750	0.7989	0.6473
0.7579	50.0	37500	0.7989	0.6473

Framework versions

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