smids_10x_deit_tiny_sgd_0001_fold3

hkivancoral/smids_10x_deit_tiny_sgd_0001_fold3

smids_10x_deit_tiny_sgd_0001_fold3 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 21, 2023
Updated: Dec 22, 2023
SHA: 717df4cb

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_10x_deit_tiny_sgd_0001_fold3

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: 0.3822
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
1.0766	1.0	750	1.0931	0.4283
0.9667	2.0	1500	1.0054	0.5083
0.9193	3.0	2250	0.9314	0.5533
0.7955	4.0	3000	0.8645	0.595
0.78	5.0	3750	0.7998	0.6283
0.6828	6.0	4500	0.7419	0.6833
0.6256	7.0	5250	0.6910	0.715
0.6533	8.0	6000	0.6501	0.7417
0.5725	9.0	6750	0.6153	0.76
0.5585	10.0	7500	0.5875	0.7733
0.5119	11.0	8250	0.5647	0.7917
0.4751	12.0	9000	0.5443	0.8033
0.4748	13.0	9750	0.5264	0.815
0.543	14.0	10500	0.5129	0.8167
0.4792	15.0	11250	0.4995	0.8233
0.4638	16.0	12000	0.4900	0.8267
0.4373	17.0	12750	0.4791	0.8267
0.4624	18.0	13500	0.4695	0.8333
0.4581	19.0	14250	0.4625	0.8317
0.4031	20.0	15000	0.4549	0.8283
0.3798	21.0	15750	0.4466	0.8367
0.4127	22.0	16500	0.4410	0.84
0.4351	23.0	17250	0.4350	0.8433
0.4464	24.0	18000	0.4308	0.835
0.4334	25.0	18750	0.4260	0.8333
0.4382	26.0	19500	0.4212	0.8367
0.3088	27.0	20250	0.4169	0.8367
0.3982	28.0	21000	0.4136	0.8367
0.4061	29.0	21750	0.4101	0.8367
0.4007	30.0	22500	0.4079	0.84
0.3333	31.0	23250	0.4046	0.8417
0.3804	32.0	24000	0.4012	0.84
0.4007	33.0	24750	0.3989	0.8417
0.4048	34.0	25500	0.3970	0.8417
0.3319	35.0	26250	0.3948	0.8433
0.3736	36.0	27000	0.3932	0.8467
0.3994	37.0	27750	0.3918	0.8483
0.3998	38.0	28500	0.3900	0.8483
0.3526	39.0	29250	0.3888	0.85
0.4438	40.0	30000	0.3872	0.8483
0.3369	41.0	30750	0.3860	0.8517
0.3716	42.0	31500	0.3855	0.8517
0.3661	43.0	32250	0.3844	0.8517
0.3454	44.0	33000	0.3840	0.8517
0.3872	45.0	33750	0.3835	0.8517
0.3238	46.0	34500	0.3830	0.8517
0.404	47.0	35250	0.3826	0.8517
0.3607	48.0	36000	0.3823	0.8517
0.3506	49.0	36750	0.3823	0.8517
0.3442	50.0	37500	0.3822	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 21, 2023
Updated: Dec 22, 2023