vit-base-aihub_model-v2

hyeongjin99/vit-base-aihub_model-v2

vit-base-aihub_model-v2 is a image classification model from hyeongjin99. 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. --

Downloads · 30 days

2% of all-time downloads

All-time downloads

492

Public

Repo size

2.4 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin343 MB · 100%

At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Jul 17, 2023
Updated: Jul 17, 2023
SHA: ce106b28

Datasets

imagefolder

Model card

From the Hugging Face model README

vit-base-aihub_model-v2

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3076
Accuracy: 0.9639
Precision: 0.9610
Recall: 0.9614
F1: 0.9604

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: 5e-05
train_batch_size: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	3	1.2753	0.8373	0.8563	0.7993	0.8022
No log	2.0	6	1.1252	0.8675	0.8895	0.8300	0.8333
No log	3.0	9	0.9427	0.8976	0.9185	0.8696	0.8760
1.1721	4.0	12	0.7995	0.9398	0.9474	0.9195	0.9246
1.1721	5.0	15	0.6820	0.9699	0.9704	0.9613	0.9642
1.1721	6.0	18	0.5927	0.9639	0.9603	0.9583	0.9587
0.7084	7.0	21	0.5239	0.9759	0.9725	0.9729	0.9725
0.7084	8.0	24	0.4743	0.9699	0.9665	0.9671	0.9665
0.7084	9.0	27	0.4436	0.9578	0.9558	0.9556	0.9544
0.4668	10.0	30	0.4070	0.9639	0.9610	0.9614	0.9604
0.4668	11.0	33	0.3817	0.9699	0.9665	0.9671	0.9665
0.4668	12.0	36	0.3625	0.9699	0.9665	0.9671	0.9665
0.4668	13.0	39	0.3536	0.9578	0.9558	0.9556	0.9544
0.3611	14.0	42	0.3384	0.9578	0.9558	0.9556	0.9544
0.3611	15.0	45	0.3249	0.9699	0.9665	0.9671	0.9665
0.3611	16.0	48	0.3164	0.9699	0.9665	0.9671	0.9665
0.3063	17.0	51	0.3142	0.9639	0.9610	0.9614	0.9604
0.3063	18.0	54	0.3122	0.9639	0.9610	0.9614	0.9604
0.3063	19.0	57	0.3093	0.9639	0.9610	0.9614	0.9604
0.294	20.0	60	0.3076	0.9639	0.9610	0.9614	0.9604

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jul 17, 2023
Updated: Jul 17, 2023