smids_5x_deit_tiny_rms_001_fold4

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: 2.2524
• Accuracy: 0.7767

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: 0.001
• 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
0.818	1.0	375	0.8019	0.5417
0.7912	2.0	750	0.8025	0.57
0.7276	3.0	1125	0.7672	0.6083
0.7922	4.0	1500	0.6983	0.6533
0.7335	5.0	1875	0.6685	0.6917
0.6959	6.0	2250	0.6471	0.7233
0.623	7.0	2625	0.6073	0.7233
0.6887	8.0	3000	0.6966	0.6667
0.6552	9.0	3375	0.5957	0.74
0.6126	10.0	3750	0.6205	0.7
0.5793	11.0	4125	0.5808	0.7567
0.6219	12.0	4500	0.5874	0.745
0.5436	13.0	4875	0.6140	0.7317
0.6012	14.0	5250	0.5834	0.7417
0.6043	15.0	5625	0.5539	0.75
0.5011	16.0	6000	0.5531	0.7383
0.5057	17.0	6375	0.5890	0.75
0.5517	18.0	6750	0.5510	0.7583
0.5553	19.0	7125	0.5435	0.76
0.5674	20.0	7500	0.4957	0.7933
0.4667	21.0	7875	0.5150	0.7867
0.4405	22.0	8250	0.5576	0.7867
0.4436	23.0	8625	0.4866	0.7967
0.454	24.0	9000	0.5354	0.775
0.4111	25.0	9375	0.5789	0.7717
0.4049	26.0	9750	0.5450	0.7817
0.397	27.0	10125	0.5808	0.7883
0.3436	28.0	10500	0.5933	0.7817
0.3249	29.0	10875	0.5969	0.7633
0.3897	30.0	11250	0.5739	0.7817
0.3938	31.0	11625	0.5794	0.7883
0.2714	32.0	12000	0.6582	0.775
0.2808	33.0	12375	0.6348	0.775
0.321	34.0	12750	0.7200	0.7567
0.2202	35.0	13125	0.6917	0.7817
0.1634	36.0	13500	0.7700	0.7733
0.3232	37.0	13875	0.7503	0.785
0.1845	38.0	14250	0.8724	0.7567
0.1357	39.0	14625	1.0521	0.7683
0.0994	40.0	15000	1.0716	0.77
0.0743	41.0	15375	1.1704	0.7717
0.1059	42.0	15750	1.2031	0.7783
0.0494	43.0	16125	1.3921	0.7633
0.0147	44.0	16500	1.5250	0.77
0.0663	45.0	16875	1.6538	0.7667
0.0618	46.0	17250	1.8210	0.765
0.0041	47.0	17625	1.9243	0.7617
0.0018	48.0	18000	2.1515	0.7717
0.0025	49.0	18375	2.2407	0.7683
0.0002	50.0	18750	2.2524	0.7767

Framework versions

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