smids_3x_deit_tiny_sgd_001_fold2

hkivancoral/smids_3x_deit_tiny_sgd_001_fold2

smids_3x_deit_tiny_sgd_001_fold2 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: 4ccc25ad

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_tiny_sgd_001_fold2

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: 0.3631
Accuracy: 0.8652

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: 0.001
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
0.9177	1.0	225	0.8996	0.5691
0.6997	2.0	450	0.6912	0.7121
0.5229	3.0	675	0.5718	0.7671
0.5533	4.0	900	0.5111	0.8020
0.4272	5.0	1125	0.4697	0.8070
0.3877	6.0	1350	0.4425	0.8170
0.4004	7.0	1575	0.4203	0.8336
0.3661	8.0	1800	0.4043	0.8369
0.3402	9.0	2025	0.3983	0.8386
0.2899	10.0	2250	0.3839	0.8486
0.3594	11.0	2475	0.3760	0.8469
0.2789	12.0	2700	0.3717	0.8502
0.2808	13.0	2925	0.3681	0.8502
0.2912	14.0	3150	0.3664	0.8552
0.2944	15.0	3375	0.3661	0.8502
0.3273	16.0	3600	0.3677	0.8552
0.2474	17.0	3825	0.3614	0.8552
0.1928	18.0	4050	0.3628	0.8569
0.2096	19.0	4275	0.3553	0.8519
0.2614	20.0	4500	0.3573	0.8552
0.2898	21.0	4725	0.3557	0.8619
0.3219	22.0	4950	0.3582	0.8536
0.3025	23.0	5175	0.3562	0.8602
0.28	24.0	5400	0.3553	0.8569
0.2538	25.0	5625	0.3547	0.8569
0.2485	26.0	5850	0.3551	0.8586
0.2246	27.0	6075	0.3556	0.8619
0.2303	28.0	6300	0.3556	0.8602
0.2272	29.0	6525	0.3568	0.8619
0.2494	30.0	6750	0.3572	0.8602
0.1942	31.0	6975	0.3593	0.8619
0.2095	32.0	7200	0.3591	0.8619
0.2432	33.0	7425	0.3587	0.8619
0.2713	34.0	7650	0.3578	0.8586
0.1998	35.0	7875	0.3599	0.8619
0.2229	36.0	8100	0.3607	0.8586
0.2109	37.0	8325	0.3599	0.8619
0.1909	38.0	8550	0.3609	0.8602
0.1902	39.0	8775	0.3619	0.8586
0.2221	40.0	9000	0.3623	0.8586
0.1747	41.0	9225	0.3610	0.8586
0.1796	42.0	9450	0.3605	0.8602
0.1695	43.0	9675	0.3624	0.8619
0.2018	44.0	9900	0.3615	0.8619
0.2591	45.0	10125	0.3627	0.8602
0.2	46.0	10350	0.3630	0.8602
0.1903	47.0	10575	0.3635	0.8619
0.1709	48.0	10800	0.3630	0.8636
0.21	49.0	11025	0.3631	0.8636
0.168	50.0	11250	0.3631	0.8652

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 12, 2023