smids_1x_beit_base_adamax_001_fold4

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: 1.7646
• Accuracy: 0.775

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
0.9222	1.0	75	0.8216	0.5567
0.8457	2.0	150	0.8398	0.57
0.8147	3.0	225	0.7493	0.6333
0.7701	4.0	300	0.7606	0.6117
0.8026	5.0	375	0.8189	0.565
0.6963	6.0	450	0.6808	0.665
0.7638	7.0	525	0.6641	0.7017
0.6601	8.0	600	0.6495	0.6833
0.6719	9.0	675	0.7134	0.66
0.5461	10.0	750	0.5791	0.7483
0.547	11.0	825	0.5859	0.7633
0.4912	12.0	900	0.5937	0.735
0.5352	13.0	975	0.5233	0.7667
0.4434	14.0	1050	0.5543	0.7617
0.4927	15.0	1125	0.7581	0.6767
0.4312	16.0	1200	0.5587	0.7667
0.3899	17.0	1275	0.6422	0.7633
0.3786	18.0	1350	0.6068	0.7783
0.4006	19.0	1425	0.6778	0.7617
0.3094	20.0	1500	0.6494	0.775
0.3319	21.0	1575	0.6363	0.765
0.2928	22.0	1650	0.7276	0.7817
0.2846	23.0	1725	0.8156	0.7733
0.1736	24.0	1800	0.7838	0.785
0.2416	25.0	1875	0.8283	0.775
0.1805	26.0	1950	0.8042	0.7867
0.1895	27.0	2025	1.0411	0.7933
0.0832	28.0	2100	1.0766	0.7983
0.099	29.0	2175	1.1178	0.7683
0.0916	30.0	2250	1.3040	0.775
0.128	31.0	2325	1.2237	0.7983
0.0775	32.0	2400	1.1999	0.79
0.0706	33.0	2475	1.4034	0.78
0.0546	34.0	2550	1.4009	0.785
0.0453	35.0	2625	1.2357	0.7917
0.0136	36.0	2700	1.4685	0.79
0.0534	37.0	2775	1.8215	0.7717
0.0751	38.0	2850	1.6150	0.7833
0.0013	39.0	2925	1.7207	0.7917
0.0466	40.0	3000	1.4737	0.785
0.0122	41.0	3075	1.5635	0.7783
0.0071	42.0	3150	1.6935	0.7783
0.0119	43.0	3225	1.6935	0.7833
0.0065	44.0	3300	1.7015	0.7883
0.0254	45.0	3375	1.7329	0.7867
0.0205	46.0	3450	1.6886	0.785
0.0082	47.0	3525	1.7094	0.7833
0.0134	48.0	3600	1.7793	0.78
0.005	49.0	3675	1.7866	0.7767
0.0132	50.0	3750	1.7646	0.775

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0