smids_3x_deit_small_rms_00001_fold4

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: 1.2799
• Accuracy: 0.8783

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
0.2819	1.0	225	0.3374	0.875
0.0966	2.0	450	0.3633	0.8733
0.0536	3.0	675	0.4166	0.8633
0.0743	4.0	900	0.4615	0.88
0.0231	5.0	1125	0.6512	0.865
0.0353	6.0	1350	0.6888	0.8783
0.0422	7.0	1575	0.8522	0.8717
0.0395	8.0	1800	0.9258	0.865
0.0246	9.0	2025	0.8665	0.8717
0.0065	10.0	2250	0.9925	0.8683
0.0002	11.0	2475	0.9127	0.875
0.0053	12.0	2700	0.9170	0.88
0.0078	13.0	2925	1.0774	0.8683
0.0094	14.0	3150	1.0384	0.8717
0.0815	15.0	3375	0.9472	0.8767
0.0	16.0	3600	1.0185	0.87
0.0	17.0	3825	1.1297	0.8733
0.0	18.0	4050	1.0917	0.865
0.0	19.0	4275	1.2078	0.865
0.0	20.0	4500	1.1817	0.8717
0.0328	21.0	4725	1.2494	0.8667
0.0221	22.0	4950	1.1544	0.87
0.0	23.0	5175	1.1616	0.87
0.0	24.0	5400	1.0994	0.875
0.0001	25.0	5625	1.1448	0.8767
0.0	26.0	5850	1.1095	0.88
0.0	27.0	6075	1.1096	0.88
0.0	28.0	6300	1.1094	0.8783
0.0	29.0	6525	1.1571	0.875
0.0	30.0	6750	1.1384	0.8833
0.0	31.0	6975	1.1678	0.875
0.0	32.0	7200	1.1864	0.8733
0.0006	33.0	7425	1.1549	0.8817
0.0	34.0	7650	1.1715	0.8833
0.0	35.0	7875	1.2200	0.8817
0.0	36.0	8100	1.1957	0.8817
0.0	37.0	8325	1.1943	0.8833
0.0	38.0	8550	1.1984	0.88
0.0	39.0	8775	1.2416	0.8817
0.0	40.0	9000	1.2319	0.8783
0.0	41.0	9225	1.2497	0.8783
0.0028	42.0	9450	1.2526	0.8783
0.0	43.0	9675	1.2621	0.8767
0.0026	44.0	9900	1.2618	0.875
0.0	45.0	10125	1.2702	0.8767
0.0	46.0	10350	1.2698	0.88
0.0	47.0	10575	1.2756	0.88
0.0	48.0	10800	1.2780	0.8783
0.0	49.0	11025	1.2789	0.8783
0.0	50.0	11250	1.2799	0.8783

Framework versions

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