smids_10x_deit_tiny_sgd_00001_fold5

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: 0.9599
• Accuracy: 0.5233

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
1.3812	1.0	750	1.2940	0.36
1.3175	2.0	1500	1.2389	0.3683
1.2691	3.0	2250	1.1984	0.3733
1.221	4.0	3000	1.1690	0.385
1.1712	5.0	3750	1.1479	0.3967
1.1485	6.0	4500	1.1321	0.4067
1.0967	7.0	5250	1.1194	0.3917
1.0947	8.0	6000	1.1088	0.4017
1.1331	9.0	6750	1.0995	0.405
1.0758	10.0	7500	1.0911	0.4167
1.0859	11.0	8250	1.0832	0.4117
1.074	12.0	9000	1.0758	0.4217
1.0354	13.0	9750	1.0688	0.4233
1.0611	14.0	10500	1.0620	0.4217
1.0504	15.0	11250	1.0556	0.425
1.0195	16.0	12000	1.0495	0.4367
1.0374	17.0	12750	1.0437	0.4383
1.0062	18.0	13500	1.0380	0.4433
1.0602	19.0	14250	1.0326	0.45
1.024	20.0	15000	1.0275	0.4533
0.9853	21.0	15750	1.0225	0.4567
1.024	22.0	16500	1.0178	0.46
1.0062	23.0	17250	1.0132	0.4617
0.9775	24.0	18000	1.0089	0.4683
0.9615	25.0	18750	1.0048	0.4733
0.9865	26.0	19500	1.0008	0.4783
0.9677	27.0	20250	0.9971	0.4867
0.9698	28.0	21000	0.9935	0.4867
0.9829	29.0	21750	0.9901	0.49
0.9556	30.0	22500	0.9870	0.49
0.963	31.0	23250	0.9840	0.4917
0.9489	32.0	24000	0.9813	0.495
0.9694	33.0	24750	0.9787	0.4967
0.9392	34.0	25500	0.9762	0.4967
0.9586	35.0	26250	0.9740	0.5
0.9291	36.0	27000	0.9720	0.5083
0.9064	37.0	27750	0.9701	0.5117
0.9352	38.0	28500	0.9684	0.5117
0.9164	39.0	29250	0.9668	0.5133
0.9501	40.0	30000	0.9654	0.515
0.8967	41.0	30750	0.9642	0.5167
0.9489	42.0	31500	0.9632	0.5167
0.9594	43.0	32250	0.9623	0.52
0.9042	44.0	33000	0.9616	0.5217
0.9218	45.0	33750	0.9610	0.5217
0.9234	46.0	34500	0.9605	0.5217
0.9392	47.0	35250	0.9602	0.5217
0.9497	48.0	36000	0.9600	0.525
0.9139	49.0	36750	0.9599	0.5233
0.8915	50.0	37500	0.9599	0.5233

Framework versions

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