frost-vision-v2-google_vit-base-patch16-224-v2024-11-11

nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-11

frost-vision-v2-google_vit-base-patch16-224-v2024-11-11 is a image classification model from nemik. 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: Nov 11, 2024
Updated: Nov 11, 2024
SHA: efa1f057

Base models

google/vit-base-patch16-224

Datasets

webdataset

Model card

From the Hugging Face model README

frost-vision-v2-google_vit-base-patch16-224-v2024-11-11

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

Loss: 0.1658
Accuracy: 0.9320
F1: 0.8224
Precision: 0.8172
Recall: 0.8278

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: 16
eval_batch_size: 8
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: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.3127	1.4085	100	0.2932	0.8940	0.6725	0.8153	0.5722
0.193	2.8169	200	0.2136	0.9190	0.7834	0.7969	0.7704
0.1503	4.2254	300	0.1815	0.9278	0.8100	0.8108	0.8093
0.1313	5.6338	400	0.1623	0.9327	0.8183	0.8415	0.7963
0.1166	7.0423	500	0.1658	0.9320	0.8224	0.8172	0.8278
0.093	8.4507	600	0.1606	0.9384	0.8405	0.8276	0.8537
0.0931	9.8592	700	0.1625	0.9366	0.8370	0.8191	0.8556
0.0733	11.2676	800	0.1714	0.9356	0.8310	0.8287	0.8333
0.0693	12.6761	900	0.1568	0.9398	0.8403	0.8475	0.8333
0.0615	14.0845	1000	0.1666	0.9342	0.8270	0.8262	0.8278
0.0562	15.4930	1100	0.1636	0.9394	0.8404	0.8420	0.8389
0.0507	16.9014	1200	0.1613	0.9401	0.8435	0.8388	0.8481
0.0552	18.3099	1300	0.1590	0.9412	0.8455	0.8447	0.8463
0.0439	19.7183	1400	0.1704	0.9394	0.8425	0.8333	0.8519
0.0367	21.1268	1500	0.1702	0.9426	0.8484	0.8523	0.8444
0.0424	22.5352	1600	0.1685	0.9394	0.8419	0.8358	0.8481
0.0306	23.9437	1700	0.1771	0.9380	0.8397	0.8262	0.8537
0.0352	25.3521	1800	0.1691	0.9401	0.8440	0.8364	0.8519
0.0323	26.7606	1900	0.1687	0.9426	0.8509	0.8409	0.8611
0.0297	28.1690	2000	0.1732	0.9401	0.8455	0.8304	0.8611
0.0229	29.5775	2100	0.1712	0.9412	0.8475	0.8360	0.8593

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Nov 11, 2024
Updated: Nov 11, 2024