smids_10x_deit_tiny_sgd_0001_fold5

hkivancoral/smids_10x_deit_tiny_sgd_0001_fold5

smids_10x_deit_tiny_sgd_0001_fold5 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 22, 2023
Updated: Dec 22, 2023
SHA: e89c9cc6

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_10x_deit_tiny_sgd_0001_fold5

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: 0.3767
Accuracy: 0.85

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
1.081	1.0	750	1.0837	0.4183
0.9664	2.0	1500	0.9960	0.4867
0.9109	3.0	2250	0.9172	0.5567
0.8062	4.0	3000	0.8454	0.6017
0.7721	5.0	3750	0.7755	0.6483
0.6595	6.0	4500	0.7101	0.6883
0.5526	7.0	5250	0.6552	0.715
0.6103	8.0	6000	0.6092	0.7533
0.6584	9.0	6750	0.5725	0.7633
0.5611	10.0	7500	0.5452	0.7983
0.5536	11.0	8250	0.5225	0.8067
0.522	12.0	9000	0.5053	0.8117
0.5314	13.0	9750	0.4913	0.8167
0.4936	14.0	10500	0.4799	0.8267
0.5195	15.0	11250	0.4692	0.8283
0.4075	16.0	12000	0.4606	0.8283
0.4566	17.0	12750	0.4529	0.8317
0.4172	18.0	13500	0.4445	0.8367
0.4556	19.0	14250	0.4390	0.83
0.4667	20.0	15000	0.4334	0.8333
0.3932	21.0	15750	0.4273	0.8333
0.4625	22.0	16500	0.4229	0.8367
0.418	23.0	17250	0.4180	0.8383
0.3957	24.0	18000	0.4143	0.8367
0.4114	25.0	18750	0.4106	0.8367
0.4039	26.0	19500	0.4070	0.8367
0.3652	27.0	20250	0.4039	0.84
0.3862	28.0	21000	0.4011	0.84
0.4364	29.0	21750	0.3984	0.84
0.3781	30.0	22500	0.3966	0.8417
0.3636	31.0	23250	0.3941	0.8383
0.3588	32.0	24000	0.3920	0.84
0.4007	33.0	24750	0.3903	0.8417
0.3328	34.0	25500	0.3888	0.84
0.3699	35.0	26250	0.3865	0.8417
0.3686	36.0	27000	0.3852	0.8433
0.315	37.0	27750	0.3840	0.8467
0.3799	38.0	28500	0.3828	0.8467
0.3659	39.0	29250	0.3817	0.8467
0.3715	40.0	30000	0.3806	0.8467
0.3582	41.0	30750	0.3798	0.8467
0.4093	42.0	31500	0.3792	0.8483
0.3651	43.0	32250	0.3786	0.8483
0.3713	44.0	33000	0.3782	0.85
0.3675	45.0	33750	0.3776	0.85
0.3336	46.0	34500	0.3773	0.85
0.4464	47.0	35250	0.3769	0.85
0.3703	48.0	36000	0.3768	0.85
0.366	49.0	36750	0.3767	0.85
0.311	50.0	37500	0.3767	0.85

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 22, 2023
Updated: Dec 22, 2023