smids_3x_deit_tiny_adamax_00001_fold2

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

• Loss: 0.9651
• Accuracy: 0.8719

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
0.4234	1.0	225	0.4415	0.8186
0.3257	2.0	450	0.3946	0.8353
0.2484	3.0	675	0.3209	0.8719
0.2642	4.0	900	0.3296	0.8702
0.1771	5.0	1125	0.3172	0.8702
0.173	6.0	1350	0.3544	0.8586
0.1484	7.0	1575	0.3447	0.8702
0.0971	8.0	1800	0.3412	0.8785
0.1373	9.0	2025	0.3898	0.8735
0.0316	10.0	2250	0.4151	0.8702
0.056	11.0	2475	0.4368	0.8669
0.0751	12.0	2700	0.5052	0.8752
0.0299	13.0	2925	0.5143	0.8769
0.0139	14.0	3150	0.5498	0.8802
0.0304	15.0	3375	0.6069	0.8752
0.0339	16.0	3600	0.6246	0.8785
0.0114	17.0	3825	0.6695	0.8735
0.0005	18.0	4050	0.7207	0.8702
0.0014	19.0	4275	0.7338	0.8669
0.0073	20.0	4500	0.7686	0.8669
0.0003	21.0	4725	0.8099	0.8669
0.0003	22.0	4950	0.8291	0.8686
0.002	23.0	5175	0.8321	0.8669
0.0005	24.0	5400	0.8587	0.8669
0.0136	25.0	5625	0.8652	0.8702
0.0002	26.0	5850	0.8692	0.8702
0.0001	27.0	6075	0.8870	0.8702
0.0001	28.0	6300	0.8976	0.8686
0.0057	29.0	6525	0.9057	0.8719
0.0181	30.0	6750	0.9160	0.8719
0.0001	31.0	6975	0.9098	0.8702
0.0001	32.0	7200	0.9072	0.8719
0.0001	33.0	7425	0.9201	0.8686
0.0001	34.0	7650	0.9257	0.8752
0.0	35.0	7875	0.9399	0.8702
0.0059	36.0	8100	0.9356	0.8686
0.0	37.0	8325	0.9495	0.8719
0.0	38.0	8550	0.9431	0.8702
0.0039	39.0	8775	0.9538	0.8702
0.0	40.0	9000	0.9587	0.8686
0.0	41.0	9225	0.9526	0.8702
0.0	42.0	9450	0.9618	0.8686
0.0	43.0	9675	0.9656	0.8719
0.0	44.0	9900	0.9609	0.8719
0.0001	45.0	10125	0.9585	0.8719
0.0	46.0	10350	0.9641	0.8702
0.0	47.0	10575	0.9675	0.8686
0.0	48.0	10800	0.9660	0.8719
0.0045	49.0	11025	0.9652	0.8719
0.0044	50.0	11250	0.9651	0.8719

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2