frost-vision-v2-google_vit-base-patch16-224-v2024-11-14

nemik/frost-vision-v2-google_vit-base-patch16-224-v2024-11-14

frost-vision-v2-google_vit-base-patch16-224-v2024-11-14 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 14, 2024
Updated: Nov 14, 2024
SHA: 70ae3791

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-v2024-11-14

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: 0.1577
Accuracy: 0.9389
F1: 0.8436
Precision: 0.8655
Recall: 0.8228

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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
0.3381	1.2346	100	0.3271	0.8660	0.5669	0.8045	0.4376
0.2067	2.4691	200	0.2080	0.9194	0.7827	0.8514	0.7242
0.1745	3.7037	300	0.1864	0.9228	0.8003	0.8308	0.7720
0.1724	4.9383	400	0.1792	0.9299	0.8188	0.8493	0.7904
0.128	6.1728	500	0.1736	0.9327	0.8292	0.8437	0.8151
0.1034	7.4074	600	0.1672	0.9355	0.8348	0.8571	0.8136
0.0944	8.6420	700	0.1579	0.9392	0.8452	0.8622	0.8290
0.0919	9.8765	800	0.1631	0.9364	0.8347	0.8710	0.8012
0.0791	11.1111	900	0.1592	0.9380	0.8383	0.8771	0.8028
0.0684	12.3457	1000	0.1577	0.9389	0.8436	0.8655	0.8228
0.0737	13.5802	1100	0.1678	0.9380	0.8416	0.8613	0.8228
0.0625	14.8148	1200	0.1646	0.9426	0.8542	0.8692	0.8398
0.0591	16.0494	1300	0.1625	0.9432	0.8549	0.8756	0.8351
0.0464	17.2840	1400	0.1722	0.9386	0.8422	0.8676	0.8182
0.048	18.5185	1500	0.1694	0.9401	0.8472	0.8663	0.8290
0.0353	19.7531	1600	0.1715	0.9392	0.8462	0.8576	0.8351
0.0434	20.9877	1700	0.1817	0.9370	0.8386	0.8618	0.8166
0.0332	22.2222	1800	0.1797	0.9383	0.8423	0.8627	0.8228
0.0283	23.4568	1900	0.1810	0.9401	0.8482	0.8617	0.8351
0.0474	24.6914	2000	0.1765	0.9398	0.8454	0.8709	0.8213
0.0365	25.9259	2100	0.1835	0.9414	0.8516	0.8637	0.8398
0.0244	27.1605	2200	0.1822	0.9404	0.8479	0.8677	0.8290
0.0242	28.3951	2300	0.1808	0.9407	0.8483	0.8703	0.8274
0.0296	29.6296	2400	0.1817	0.9401	0.8477	0.864	0.8320

Framework versions

Transformers 4.46.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Nov 14, 2024
Updated: Nov 14, 2024