smids_3x_beit_base_rms_001_fold3

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

• Loss: 0.6251
• Accuracy: 0.7617

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
0.9148	1.0	225	0.9238	0.505
0.8709	2.0	450	0.9060	0.515
0.8398	3.0	675	0.8688	0.5317
0.74	4.0	900	0.7859	0.5617
0.7787	5.0	1125	0.7847	0.6017
0.7532	6.0	1350	0.7702	0.63
0.7432	7.0	1575	0.7450	0.655
0.7264	8.0	1800	0.7610	0.6317
0.7321	9.0	2025	0.7293	0.655
0.6592	10.0	2250	0.7888	0.6367
0.7528	11.0	2475	0.7158	0.6633
0.7282	12.0	2700	0.7365	0.64
0.6884	13.0	2925	0.6939	0.6733
0.6852	14.0	3150	0.7006	0.67
0.6011	15.0	3375	0.7591	0.6233
0.6904	16.0	3600	0.6846	0.6717
0.6393	17.0	3825	0.6741	0.7117
0.6772	18.0	4050	0.6655	0.6683
0.6409	19.0	4275	0.6658	0.6933
0.5941	20.0	4500	0.6429	0.7017
0.5753	21.0	4725	0.6753	0.6833
0.5975	22.0	4950	0.6543	0.6917
0.5954	23.0	5175	0.6358	0.7233
0.5729	24.0	5400	0.6341	0.7133
0.6313	25.0	5625	0.6336	0.7033
0.5938	26.0	5850	0.6447	0.7083
0.5183	27.0	6075	0.6247	0.7233
0.5713	28.0	6300	0.6145	0.73
0.5948	29.0	6525	0.5934	0.7317
0.5273	30.0	6750	0.5971	0.7367
0.5431	31.0	6975	0.5930	0.7433
0.6025	32.0	7200	0.6434	0.7183
0.5898	33.0	7425	0.5982	0.7383
0.5455	34.0	7650	0.5983	0.75
0.4857	35.0	7875	0.6162	0.735
0.5822	36.0	8100	0.5546	0.7517
0.4869	37.0	8325	0.5748	0.745
0.4722	38.0	8550	0.5753	0.7417
0.4982	39.0	8775	0.5694	0.7483
0.4478	40.0	9000	0.5912	0.74
0.4295	41.0	9225	0.5914	0.75
0.4581	42.0	9450	0.5846	0.7617
0.3797	43.0	9675	0.5733	0.7667
0.4086	44.0	9900	0.6072	0.7517
0.4164	45.0	10125	0.6033	0.7583
0.3774	46.0	10350	0.6024	0.75
0.392	47.0	10575	0.5976	0.7617
0.3586	48.0	10800	0.6199	0.76
0.3854	49.0	11025	0.6198	0.7667
0.3586	50.0	11250	0.6251	0.7617

Framework versions

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