hushem_40x_deit_base_sgd_00001_fold1

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: 1.4455
• Accuracy: 0.2889

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
1.379	1.0	215	1.4681	0.2889
1.3967	2.0	430	1.4670	0.2889
1.423	3.0	645	1.4660	0.2889
1.4018	4.0	860	1.4650	0.2889
1.3899	5.0	1075	1.4640	0.2889
1.4076	6.0	1290	1.4631	0.2889
1.3743	7.0	1505	1.4622	0.2889
1.3724	8.0	1720	1.4613	0.2889
1.3757	9.0	1935	1.4604	0.2889
1.3783	10.0	2150	1.4596	0.2889
1.4141	11.0	2365	1.4589	0.2889
1.3702	12.0	2580	1.4581	0.2889
1.3842	13.0	2795	1.4574	0.2889
1.3926	14.0	3010	1.4567	0.2889
1.3764	15.0	3225	1.4560	0.2889
1.3955	16.0	3440	1.4553	0.2889
1.3752	17.0	3655	1.4547	0.2889
1.3872	18.0	3870	1.4541	0.2889
1.3795	19.0	4085	1.4535	0.2889
1.3768	20.0	4300	1.4530	0.2889
1.3609	21.0	4515	1.4524	0.2889
1.3552	22.0	4730	1.4519	0.2889
1.3869	23.0	4945	1.4514	0.2889
1.3741	24.0	5160	1.4510	0.2889
1.3721	25.0	5375	1.4505	0.2889
1.3593	26.0	5590	1.4501	0.2889
1.3536	27.0	5805	1.4497	0.2889
1.3543	28.0	6020	1.4493	0.2889
1.3589	29.0	6235	1.4489	0.2889
1.3445	30.0	6450	1.4486	0.2889
1.3539	31.0	6665	1.4483	0.2889
1.3535	32.0	6880	1.4480	0.2889
1.3498	33.0	7095	1.4477	0.2889
1.3497	34.0	7310	1.4474	0.2889
1.3582	35.0	7525	1.4472	0.2889
1.354	36.0	7740	1.4469	0.2889
1.3681	37.0	7955	1.4467	0.2889
1.346	38.0	8170	1.4465	0.2889
1.3468	39.0	8385	1.4463	0.2889
1.3488	40.0	8600	1.4462	0.2889
1.3542	41.0	8815	1.4460	0.2889
1.3813	42.0	9030	1.4459	0.2889
1.3585	43.0	9245	1.4458	0.2889
1.3347	44.0	9460	1.4457	0.2889
1.3527	45.0	9675	1.4456	0.2889
1.3601	46.0	9890	1.4456	0.2889
1.3484	47.0	10105	1.4455	0.2889
1.3543	48.0	10320	1.4455	0.2889
1.3639	49.0	10535	1.4455	0.2889
1.3697	50.0	10750	1.4455	0.2889

Framework versions

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