smids_5x_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: 1.0692
• Accuracy: 0.45

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
1.4013	1.0	375	1.3267	0.3533
1.3461	2.0	750	1.2947	0.36
1.2972	3.0	1125	1.2670	0.3617
1.31	4.0	1500	1.2433	0.3683
1.2221	5.0	1875	1.2236	0.3717
1.2656	6.0	2250	1.2067	0.375
1.2312	7.0	2625	1.1923	0.3817
1.1861	8.0	3000	1.1803	0.3817
1.1289	9.0	3375	1.1699	0.385
1.218	10.0	3750	1.1612	0.3867
1.1921	11.0	4125	1.1535	0.3983
1.1315	12.0	4500	1.1468	0.405
1.1732	13.0	4875	1.1407	0.4133
1.1412	14.0	5250	1.1354	0.41
1.1502	15.0	5625	1.1305	0.4133
1.126	16.0	6000	1.1259	0.4133
1.1098	17.0	6375	1.1217	0.4117
1.1197	18.0	6750	1.1177	0.4083
1.1329	19.0	7125	1.1140	0.4083
1.0741	20.0	7500	1.1105	0.4117
1.0617	21.0	7875	1.1072	0.4117
1.0917	22.0	8250	1.1041	0.41
1.0822	23.0	8625	1.1011	0.41
1.1336	24.0	9000	1.0984	0.4167
1.0665	25.0	9375	1.0958	0.415
1.1097	26.0	9750	1.0934	0.415
1.0499	27.0	10125	1.0911	0.4183
1.1202	28.0	10500	1.0889	0.4167
1.1038	29.0	10875	1.0869	0.4283
1.0838	30.0	11250	1.0850	0.4333
1.0717	31.0	11625	1.0832	0.4367
1.0773	32.0	12000	1.0816	0.4383
1.0858	33.0	12375	1.0800	0.44
1.0072	34.0	12750	1.0786	0.44
1.0435	35.0	13125	1.0773	0.4417
1.047	36.0	13500	1.0761	0.4433
1.0361	37.0	13875	1.0750	0.4483
1.0477	38.0	14250	1.0740	0.45
1.0658	39.0	14625	1.0731	0.4483
1.0711	40.0	15000	1.0723	0.445
1.0473	41.0	15375	1.0716	0.445
1.0521	42.0	15750	1.0711	0.445
1.0368	43.0	16125	1.0705	0.4467
1.0636	44.0	16500	1.0701	0.4483
1.0424	45.0	16875	1.0698	0.4483
1.0442	46.0	17250	1.0695	0.45
1.0667	47.0	17625	1.0694	0.45
1.0523	48.0	18000	1.0693	0.45
1.0135	49.0	18375	1.0692	0.45
1.0393	50.0	18750	1.0692	0.45

Framework versions

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