smids_3x_deit_tiny_rms_0001_fold1

hkivancoral/smids_3x_deit_tiny_rms_0001_fold1

smids_3x_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 12, 2023
Updated: Dec 13, 2023
SHA: d6fa6f0d

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_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: 0.8923
Accuracy: 0.8982

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
0.4584	1.0	226	0.3564	0.8414
0.3047	2.0	452	0.3557	0.8614
0.2416	3.0	678	0.4578	0.8097
0.1428	4.0	904	0.4233	0.8631
0.1227	5.0	1130	0.4440	0.8698
0.1524	6.0	1356	0.3981	0.8915
0.0691	7.0	1582	0.5148	0.8932
0.0615	8.0	1808	0.6305	0.8581
0.1111	9.0	2034	0.5927	0.8848
0.0229	10.0	2260	0.6397	0.8932
0.0429	11.0	2486	0.8816	0.8497
0.0206	12.0	2712	0.6220	0.8815
0.0864	13.0	2938	0.9519	0.8481
0.0084	14.0	3164	0.6931	0.8898
0.0377	15.0	3390	0.7392	0.8815
0.0362	16.0	3616	0.8551	0.8581
0.0572	17.0	3842	0.7247	0.8698
0.1009	18.0	4068	0.6228	0.8715
0.0504	19.0	4294	0.8902	0.8648
0.0251	20.0	4520	0.7786	0.8831
0.0065	21.0	4746	0.9795	0.8781
0.0845	22.0	4972	0.7973	0.8815
0.0036	23.0	5198	0.8709	0.8648
0.0477	24.0	5424	0.8349	0.8781
0.0002	25.0	5650	0.8199	0.8781
0.0014	26.0	5876	1.0541	0.8648
0.0123	27.0	6102	0.9016	0.8798
0.0001	28.0	6328	0.7666	0.8881
0.0	29.0	6554	0.7915	0.8881
0.0004	30.0	6780	0.9036	0.8881
0.0001	31.0	7006	0.8349	0.8932
0.0	32.0	7232	0.8309	0.8848
0.0025	33.0	7458	0.8690	0.8932
0.0	34.0	7684	0.9464	0.8848
0.0312	35.0	7910	0.9721	0.8881
0.0	36.0	8136	0.9282	0.8848
0.0	37.0	8362	0.8805	0.8915
0.0	38.0	8588	0.8709	0.8948
0.0	39.0	8814	0.8888	0.8915
0.0	40.0	9040	0.8303	0.9015
0.0028	41.0	9266	0.8497	0.8965
0.0031	42.0	9492	0.8601	0.8932
0.0	43.0	9718	0.8400	0.8932
0.0	44.0	9944	0.8634	0.8965
0.0	45.0	10170	0.8516	0.9015
0.0	46.0	10396	0.9026	0.8948
0.0	47.0	10622	0.8950	0.9015
0.0	48.0	10848	0.8925	0.8998
0.0	49.0	11074	0.8907	0.8982
0.0	50.0	11300	0.8923	0.8982

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 12, 2023
Updated: Dec 13, 2023