hushem_40x_deit_base_sgd_00001_fold3

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

• Loss: 1.4462
• Accuracy: 0.2326

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
1.395	1.0	217	1.4656	0.2326
1.3668	2.0	434	1.4647	0.2326
1.3731	3.0	651	1.4638	0.2326
1.4025	4.0	868	1.4629	0.2326
1.3639	5.0	1085	1.4620	0.2326
1.3879	6.0	1302	1.4612	0.2326
1.3778	7.0	1519	1.4604	0.2326
1.3612	8.0	1736	1.4596	0.2326
1.3705	9.0	1953	1.4589	0.2326
1.3789	10.0	2170	1.4582	0.2326
1.361	11.0	2387	1.4575	0.2326
1.3859	12.0	2604	1.4568	0.2326
1.3896	13.0	2821	1.4562	0.2326
1.3672	14.0	3038	1.4556	0.2326
1.3726	15.0	3255	1.4550	0.2326
1.3714	16.0	3472	1.4544	0.2326
1.3679	17.0	3689	1.4539	0.2326
1.3686	18.0	3906	1.4533	0.2326
1.3759	19.0	4123	1.4528	0.2326
1.3715	20.0	4340	1.4524	0.2326
1.3586	21.0	4557	1.4519	0.2326
1.3602	22.0	4774	1.4515	0.2326
1.3784	23.0	4991	1.4511	0.2326
1.3361	24.0	5208	1.4507	0.2326
1.3639	25.0	5425	1.4503	0.2326
1.3719	26.0	5642	1.4499	0.2326
1.37	27.0	5859	1.4496	0.2326
1.3566	28.0	6076	1.4493	0.2326
1.3167	29.0	6293	1.4490	0.2326
1.3522	30.0	6510	1.4487	0.2326
1.3828	31.0	6727	1.4484	0.2326
1.3664	32.0	6944	1.4481	0.2326
1.3407	33.0	7161	1.4479	0.2326
1.3633	34.0	7378	1.4477	0.2326
1.3611	35.0	7595	1.4475	0.2326
1.3425	36.0	7812	1.4473	0.2326
1.3418	37.0	8029	1.4471	0.2326
1.3479	38.0	8246	1.4470	0.2326
1.3576	39.0	8463	1.4468	0.2326
1.3408	40.0	8680	1.4467	0.2326
1.3433	41.0	8897	1.4466	0.2326
1.3828	42.0	9114	1.4465	0.2326
1.3558	43.0	9331	1.4464	0.2326
1.3616	44.0	9548	1.4463	0.2326
1.36	45.0	9765	1.4463	0.2326
1.3465	46.0	9982	1.4462	0.2326
1.3484	47.0	10199	1.4462	0.2326
1.3826	48.0	10416	1.4462	0.2326
1.3656	49.0	10633	1.4462	0.2326
1.3363	50.0	10850	1.4462	0.2326

Framework versions

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