smids_5x_deit_tiny_rms_0001_fold1

hkivancoral/smids_5x_deit_tiny_rms_0001_fold1

smids_5x_deit_tiny_rms_0001_fold1 is a image classification model from hkivancoral. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Dec 16, 2023
Updated: Dec 16, 2023
SHA: 5b36a8f4

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_5x_deit_tiny_rms_0001_fold1

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: 0.9972
Accuracy: 0.9048

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: 0.0001
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
0.2949	1.0	376	0.4792	0.7896
0.1877	2.0	752	0.3869	0.8631
0.1943	3.0	1128	0.4273	0.8514
0.1151	4.0	1504	0.4170	0.8932
0.1309	5.0	1880	0.4159	0.8748
0.0937	6.0	2256	0.5222	0.8831
0.0299	7.0	2632	0.5974	0.8932
0.0659	8.0	3008	0.6171	0.8715
0.0586	9.0	3384	0.7200	0.8781
0.0715	10.0	3760	0.9149	0.8664
0.0752	11.0	4136	0.7964	0.8765
0.0401	12.0	4512	0.6968	0.8831
0.0094	13.0	4888	0.6898	0.8865
0.0111	14.0	5264	0.7411	0.8932
0.0334	15.0	5640	0.8411	0.8798
0.0369	16.0	6016	0.7849	0.8798
0.0017	17.0	6392	0.7191	0.8898
0.0026	18.0	6768	0.8047	0.8815
0.0265	19.0	7144	0.6550	0.8982
0.0527	20.0	7520	0.7590	0.8798
0.0052	21.0	7896	0.7860	0.8881
0.001	22.0	8272	0.8487	0.8965
0.0432	23.0	8648	0.8524	0.8865
0.0032	24.0	9024	0.8174	0.9015
0.0001	25.0	9400	0.8214	0.8815
0.0146	26.0	9776	0.9080	0.8765
0.0	27.0	10152	0.8028	0.9032
0.0001	28.0	10528	0.9579	0.8915
0.0043	29.0	10904	0.8349	0.8982
0.0053	30.0	11280	0.9140	0.8831
0.0204	31.0	11656	0.9273	0.8898
0.0001	32.0	12032	0.9480	0.8848
0.0006	33.0	12408	1.0366	0.8865
0.0042	34.0	12784	1.0682	0.8798
0.0025	35.0	13160	0.9542	0.8932
0.0006	36.0	13536	0.8930	0.9048
0.0001	37.0	13912	0.9451	0.8932
0.0112	38.0	14288	1.0303	0.8848
0.0	39.0	14664	1.0298	0.8932
0.0	40.0	15040	0.9996	0.8932
0.0	41.0	15416	0.9909	0.8998
0.0	42.0	15792	0.9652	0.9015
0.0	43.0	16168	0.9547	0.9032
0.0	44.0	16544	0.9994	0.8982
0.0	45.0	16920	0.9802	0.9015
0.003	46.0	17296	0.9911	0.9032
0.0	47.0	17672	0.9936	0.9048
0.0	48.0	18048	0.9937	0.9048
0.0	49.0	18424	0.9932	0.9048
0.0025	50.0	18800	0.9972	0.9048

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 16, 2023
Updated: Dec 16, 2023