smids_3x_deit_small_rms_0001_fold1

hkivancoral/smids_3x_deit_small_rms_0001_fold1

smids_3x_deit_small_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 12, 2023
SHA: 5447648d

Base models

facebook/deit-small-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_small_rms_0001_fold1

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

Loss: 0.8635
Accuracy: 0.9065

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
0.3954	1.0	226	0.3893	0.8280
0.2465	2.0	452	0.3428	0.8881
0.1728	3.0	678	0.3745	0.8831
0.1347	4.0	904	0.4194	0.8848
0.0709	5.0	1130	0.4431	0.8898
0.0807	6.0	1356	0.4957	0.8781
0.0722	7.0	1582	0.4748	0.8898
0.036	8.0	1808	0.6178	0.8831
0.0808	9.0	2034	0.5850	0.8831
0.0404	10.0	2260	0.5350	0.9015
0.016	11.0	2486	0.5574	0.8831
0.0147	12.0	2712	0.5709	0.8865
0.0113	13.0	2938	0.6888	0.8815
0.0209	14.0	3164	0.4757	0.9149
0.0245	15.0	3390	0.6913	0.8815
0.0203	16.0	3616	0.6653	0.8865
0.0109	17.0	3842	0.7353	0.8898
0.0341	18.0	4068	0.7660	0.8865
0.0053	19.0	4294	0.6013	0.8965
0.0015	20.0	4520	0.6073	0.8965
0.0003	21.0	4746	0.7366	0.8965
0.0274	22.0	4972	0.7587	0.8798
0.0019	23.0	5198	0.6702	0.8998
0.0001	24.0	5424	0.7767	0.8815
0.0008	25.0	5650	0.6634	0.8998
0.023	26.0	5876	0.7380	0.8915
0.0	27.0	6102	0.8025	0.8898
0.0797	28.0	6328	0.7171	0.8948
0.0492	29.0	6554	0.6827	0.8982
0.0	30.0	6780	0.7690	0.9048
0.0	31.0	7006	0.7411	0.9048
0.0	32.0	7232	0.7425	0.8965
0.0032	33.0	7458	0.7178	0.9115
0.0006	34.0	7684	0.7893	0.9082
0.0	35.0	7910	0.8185	0.8932
0.0181	36.0	8136	0.8745	0.8932
0.0003	37.0	8362	0.8672	0.8932
0.0	38.0	8588	0.8314	0.8982
0.0	39.0	8814	0.8333	0.8965
0.0	40.0	9040	0.7854	0.9065
0.0036	41.0	9266	0.8828	0.9015
0.0028	42.0	9492	0.8402	0.9065
0.0	43.0	9718	0.8689	0.8982
0.0	44.0	9944	0.8390	0.9065
0.0	45.0	10170	0.8434	0.9082
0.0	46.0	10396	0.8531	0.9132
0.0	47.0	10622	0.8589	0.9098
0.0	48.0	10848	0.8632	0.9065
0.0	49.0	11074	0.8630	0.9065
0.0	50.0	11300	0.8635	0.9065

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+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 12, 2023