smids_1x_deit_tiny_adamax_0001_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: 0.8884
• Accuracy: 0.8735

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: 0.0001
• 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
0.4433	1.0	75	0.4087	0.8136
0.2718	2.0	150	0.3393	0.8636
0.1523	3.0	225	0.3549	0.8602
0.1126	4.0	300	0.4203	0.8785
0.1857	5.0	375	0.5016	0.8702
0.1676	6.0	450	0.6812	0.8403
0.0685	7.0	525	0.6011	0.8719
0.0238	8.0	600	0.6670	0.8686
0.0519	9.0	675	0.6013	0.8686
0.0386	10.0	750	0.7008	0.8719
0.0148	11.0	825	0.7193	0.8619
0.0007	12.0	900	0.7563	0.8752
0.0241	13.0	975	0.7693	0.8636
0.0307	14.0	1050	0.8760	0.8636
0.0115	15.0	1125	0.7808	0.8719
0.0035	16.0	1200	0.7588	0.8669
0.0001	17.0	1275	0.8971	0.8619
0.0102	18.0	1350	0.7909	0.8719
0.0001	19.0	1425	0.7984	0.8636
0.004	20.0	1500	0.8206	0.8669
0.0001	21.0	1575	0.8515	0.8752
0.0	22.0	1650	0.7887	0.8752
0.0	23.0	1725	0.9036	0.8719
0.0001	24.0	1800	0.8151	0.8735
0.0	25.0	1875	0.8674	0.8669
0.0	26.0	1950	0.8463	0.8702
0.0044	27.0	2025	0.8541	0.8669
0.0043	28.0	2100	0.8322	0.8669
0.002	29.0	2175	0.8405	0.8686
0.0043	30.0	2250	0.8433	0.8686
0.0034	31.0	2325	0.8353	0.8752
0.0	32.0	2400	0.8744	0.8702
0.0	33.0	2475	0.8688	0.8669
0.0	34.0	2550	0.8557	0.8669
0.0091	35.0	2625	0.8746	0.8669
0.0	36.0	2700	0.8586	0.8686
0.0	37.0	2775	0.8715	0.8702
0.0	38.0	2850	0.8844	0.8719
0.0019	39.0	2925	0.8957	0.8735
0.0	40.0	3000	0.8803	0.8752
0.0031	41.0	3075	0.8802	0.8752
0.0	42.0	3150	0.8828	0.8752
0.0026	43.0	3225	0.8803	0.8735
0.003	44.0	3300	0.8878	0.8752
0.0	45.0	3375	0.8894	0.8702
0.0	46.0	3450	0.8874	0.8735
0.0024	47.0	3525	0.8958	0.8752
0.0	48.0	3600	0.8893	0.8735
0.0023	49.0	3675	0.8880	0.8735
0.0021	50.0	3750	0.8884	0.8735

Framework versions

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