smids_3x_deit_small_adamax_00001_fold2

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

• Loss: 0.9125
• 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: 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
0.3392	1.0	225	0.3664	0.8502
0.2605	2.0	450	0.3323	0.8719
0.212	3.0	675	0.3215	0.8686
0.2229	4.0	900	0.3309	0.8652
0.106	5.0	1125	0.3345	0.8802
0.0845	6.0	1350	0.3616	0.8719
0.0626	7.0	1575	0.3907	0.8686
0.0326	8.0	1800	0.4483	0.8669
0.0372	9.0	2025	0.4833	0.8652
0.0087	10.0	2250	0.5521	0.8735
0.0217	11.0	2475	0.5679	0.8752
0.0111	12.0	2700	0.6269	0.8702
0.011	13.0	2925	0.6480	0.8702
0.0061	14.0	3150	0.6728	0.8686
0.0004	15.0	3375	0.7336	0.8669
0.0093	16.0	3600	0.7662	0.8702
0.0044	17.0	3825	0.7704	0.8752
0.0001	18.0	4050	0.7907	0.8735
0.0005	19.0	4275	0.7929	0.8669
0.0001	20.0	4500	0.8179	0.8669
0.0001	21.0	4725	0.8135	0.8785
0.0001	22.0	4950	0.8581	0.8702
0.0037	23.0	5175	0.8366	0.8719
0.0001	24.0	5400	0.8672	0.8686
0.0168	25.0	5625	0.8621	0.8686
0.0001	26.0	5850	0.8633	0.8702
0.0	27.0	6075	0.8679	0.8669
0.0001	28.0	6300	0.8863	0.8735
0.0001	29.0	6525	0.8794	0.8686
0.0145	30.0	6750	0.8923	0.8686
0.0	31.0	6975	0.8806	0.8719
0.0	32.0	7200	0.8844	0.8686
0.0001	33.0	7425	0.8917	0.8669
0.0	34.0	7650	0.8891	0.8719
0.0	35.0	7875	0.8984	0.8735
0.0077	36.0	8100	0.8879	0.8752
0.0	37.0	8325	0.9058	0.8702
0.0	38.0	8550	0.9002	0.8686
0.0096	39.0	8775	0.9018	0.8752
0.0	40.0	9000	0.9051	0.8752
0.0	41.0	9225	0.9023	0.8702
0.0	42.0	9450	0.9103	0.8752
0.0	43.0	9675	0.9151	0.8735
0.0	44.0	9900	0.9097	0.8735
0.0	45.0	10125	0.9063	0.8702
0.0	46.0	10350	0.9129	0.8735
0.0	47.0	10575	0.9170	0.8735
0.0	48.0	10800	0.9138	0.8735
0.0048	49.0	11025	0.9128	0.8735
0.0048	50.0	11250	0.9125	0.8735

Framework versions

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