smids_5x_deit_small_rms_00001_fold1

hkivancoral/smids_5x_deit_small_rms_00001_fold1

smids_5x_deit_small_rms_00001_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 18, 2023
Updated: Dec 18, 2023
SHA: 27177607

Base models

facebook/deit-small-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_5x_deit_small_rms_00001_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: 0.7417
Accuracy: 0.9232

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: 1e-05
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
0.2432	1.0	376	0.2964	0.8731
0.1614	2.0	752	0.2849	0.8932
0.1382	3.0	1128	0.2988	0.9048
0.0674	4.0	1504	0.3782	0.9082
0.0238	5.0	1880	0.4767	0.9132
0.0038	6.0	2256	0.4683	0.9215
0.0081	7.0	2632	0.5149	0.9015
0.0311	8.0	3008	0.6215	0.9082
0.0258	9.0	3384	0.6420	0.9015
0.0003	10.0	3760	0.7389	0.8982
0.0016	11.0	4136	0.7097	0.9032
0.0237	12.0	4512	0.7322	0.8965
0.0	13.0	4888	0.6330	0.9065
0.0121	14.0	5264	0.6713	0.8998
0.0128	15.0	5640	0.6959	0.9032
0.0	16.0	6016	0.5921	0.9165
0.0147	17.0	6392	0.7286	0.9032
0.0096	18.0	6768	0.6654	0.9115
0.0001	19.0	7144	0.7241	0.9065
0.026	20.0	7520	0.7595	0.9115
0.0004	21.0	7896	0.7089	0.9132
0.0001	22.0	8272	0.7020	0.9132
0.0189	23.0	8648	0.7064	0.9032
0.0	24.0	9024	0.6953	0.9182
0.0008	25.0	9400	0.6754	0.9048
0.0029	26.0	9776	0.6682	0.9149
0.0039	27.0	10152	0.7036	0.9115
0.0	28.0	10528	0.7901	0.9098
0.0047	29.0	10904	0.7958	0.9165
0.0042	30.0	11280	0.7246	0.9115
0.0	31.0	11656	0.7694	0.9132
0.0	32.0	12032	0.7581	0.9082
0.0	33.0	12408	0.7146	0.9149
0.0	34.0	12784	0.7034	0.9165
0.0	35.0	13160	0.7688	0.9115
0.0	36.0	13536	0.7638	0.9132
0.0	37.0	13912	0.8028	0.9115
0.0	38.0	14288	0.7323	0.9215
0.0	39.0	14664	0.7555	0.9199
0.0	40.0	15040	0.7506	0.9215
0.0	41.0	15416	0.7416	0.9215
0.0	42.0	15792	0.7376	0.9199
0.0	43.0	16168	0.7280	0.9215
0.0	44.0	16544	0.7390	0.9215
0.0	45.0	16920	0.7365	0.9232
0.0028	46.0	17296	0.7367	0.9232
0.0	47.0	17672	0.7395	0.9232
0.0	48.0	18048	0.7408	0.9232
0.0	49.0	18424	0.7418	0.9232
0.0024	50.0	18800	0.7417	0.9232

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 18, 2023
Updated: Dec 18, 2023