smids_10x_beit_large_sgd_00001_fold1

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: 0.8308
• Accuracy: 0.6361

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
1.1798	1.0	751	1.2530	0.3139
1.1588	2.0	1502	1.2210	0.3272
1.0286	3.0	2253	1.1929	0.3272
1.0699	4.0	3004	1.1680	0.3372
1.0532	5.0	3755	1.1455	0.3539
1.0168	6.0	4506	1.1249	0.3589
1.0334	7.0	5257	1.1059	0.3840
1.006	8.0	6008	1.0879	0.3923
0.9781	9.0	6759	1.0713	0.4073
0.9206	10.0	7510	1.0557	0.4324
0.9599	11.0	8261	1.0410	0.4457
0.8538	12.0	9012	1.0272	0.4591
0.8992	13.0	9763	1.0143	0.4725
0.9105	14.0	10514	1.0019	0.4925
0.8886	15.0	11265	0.9904	0.5058
0.8635	16.0	12016	0.9792	0.5209
0.9091	17.0	12767	0.9687	0.5292
0.8236	18.0	13518	0.9588	0.5342
0.8559	19.0	14269	0.9493	0.5426
0.7879	20.0	15020	0.9403	0.5509
0.765	21.0	15771	0.9320	0.5543
0.8223	22.0	16522	0.9238	0.5593
0.782	23.0	17273	0.9162	0.5659
0.875	24.0	18024	0.9090	0.5726
0.8022	25.0	18775	0.9023	0.5793
0.8471	26.0	19526	0.8959	0.5860
0.7822	27.0	20277	0.8898	0.5977
0.789	28.0	21028	0.8841	0.6010
0.8149	29.0	21779	0.8788	0.6027
0.7987	30.0	22530	0.8738	0.6077
0.7188	31.0	23281	0.8692	0.6160
0.802	32.0	24032	0.8649	0.6194
0.8114	33.0	24783	0.8608	0.6194
0.7414	34.0	25534	0.8570	0.6210
0.766	35.0	26285	0.8536	0.6210
0.7537	36.0	27036	0.8504	0.6260
0.7794	37.0	27787	0.8475	0.6277
0.7455	38.0	28538	0.8448	0.6311
0.7702	39.0	29289	0.8424	0.6311
0.75	40.0	30040	0.8403	0.6311
0.7442	41.0	30791	0.8384	0.6344
0.6885	42.0	31542	0.8367	0.6344
0.7317	43.0	32293	0.8353	0.6344
0.7377	44.0	33044	0.8340	0.6344
0.7327	45.0	33795	0.8330	0.6344
0.752	46.0	34546	0.8322	0.6361
0.7091	47.0	35297	0.8315	0.6361
0.7684	48.0	36048	0.8311	0.6361
0.7425	49.0	36799	0.8309	0.6361
0.7641	50.0	37550	0.8308	0.6361

Framework versions

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