smids_3x_deit_base_rms_00001_fold2

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

• Loss: 1.1006
• Accuracy: 0.8802

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
0.1997	1.0	225	0.3294	0.8636
0.1218	2.0	450	0.3458	0.8752
0.0584	3.0	675	0.3858	0.8852
0.0328	4.0	900	0.5094	0.8819
0.0105	5.0	1125	0.6264	0.8669
0.0195	6.0	1350	0.6524	0.8819
0.0393	7.0	1575	0.7580	0.8785
0.0118	8.0	1800	0.8225	0.8752
0.0025	9.0	2025	0.8445	0.8885
0.0015	10.0	2250	0.9017	0.8752
0.0012	11.0	2475	0.8480	0.8885
0.0006	12.0	2700	0.8747	0.8802
0.0003	13.0	2925	0.8028	0.8819
0.0034	14.0	3150	0.8751	0.8769
0.0001	15.0	3375	0.8609	0.8852
0.0001	16.0	3600	0.9267	0.8769
0.0057	17.0	3825	0.9169	0.8735
0.036	18.0	4050	0.8611	0.8885
0.0	19.0	4275	0.8854	0.8785
0.0034	20.0	4500	1.0598	0.8669
0.0	21.0	4725	0.9577	0.8752
0.0	22.0	4950	0.9365	0.8686
0.0032	23.0	5175	0.9251	0.8785
0.0	24.0	5400	0.9847	0.8686
0.0035	25.0	5625	1.0144	0.8719
0.0	26.0	5850	1.0411	0.8636
0.0	27.0	6075	1.0565	0.8669
0.0	28.0	6300	1.0364	0.8702
0.0	29.0	6525	1.0760	0.8785
0.003	30.0	6750	1.0560	0.8785
0.0	31.0	6975	0.9941	0.8835
0.0	32.0	7200	1.0698	0.8735
0.0	33.0	7425	1.0500	0.8769
0.0	34.0	7650	0.9900	0.8852
0.0	35.0	7875	1.1120	0.8735
0.0032	36.0	8100	1.0645	0.8819
0.0	37.0	8325	1.0762	0.8802
0.0	38.0	8550	1.0820	0.8819
0.0032	39.0	8775	1.0824	0.8769
0.0	40.0	9000	1.0804	0.8769
0.0	41.0	9225	1.0824	0.8785
0.0	42.0	9450	1.0865	0.8819
0.0	43.0	9675	1.0882	0.8802
0.0	44.0	9900	1.0914	0.8802
0.0	45.0	10125	1.0895	0.8819
0.0	46.0	10350	1.0934	0.8819
0.0	47.0	10575	1.0962	0.8819
0.0	48.0	10800	1.0971	0.8819
0.0022	49.0	11025	1.0991	0.8802
0.0022	50.0	11250	1.1006	0.8802

Framework versions

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