2025-02-05-14-22-36-vit-base-patch16-224 – AI Model by alyzbane | AlphaNeural AI

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alyzbane/
2025-02-05-14-22-36-vit-base-patch16-224

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image-classification

generated_from_trainer

google/vit-base-patch16-224

autotrain_compatible

endpoints_compatible

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2025-02-05-14-22-36-vit-base-patch16-224

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

Loss: 0.0410
Precision: 0.9931
Recall: 0.9927
F1: 0.9927
Accuracy: 0.9931
Top1 Accuracy: 0.9927
Error Rate: 0.0069

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: 0.0002
train_batch_size: 32
eval_batch_size: 32
seed: 3407
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Top1 Accuracy	Error Rate
0.9229	1.0	103	0.2079	0.9541	0.9366	0.9386	0.9333	0.9366	0.0667
0.1707	2.0	206	0.1222	0.9726	0.9707	0.9688	0.9664	0.9707	0.0336
0.0937	3.0	309	0.1807	0.9552	0.9512	0.9505	0.9561	0.9512	0.0439
0.0619	4.0	412	0.1122	0.9764	0.9756	0.9754	0.9778	0.9756	0.0222
0.0309	5.0	515	0.0803	0.9884	0.9878	0.9875	0.9863	0.9878	0.0137
0.0275	6.0	618	0.0437	0.9904	0.9902	0.9902	0.9905	0.9902	0.0095
0.0103	7.0	721	0.0426	0.9905	0.9902	0.9903	0.9908	0.9902	0.0092
0.0065	8.0	824	0.0414	0.9953	0.9951	0.9951	0.9949	0.9951	0.0051
0.004	9.0	927	0.0410	0.9931	0.9927	0.9927	0.9931	0.9927	0.0069
0.0007	10.0	1030	0.0416	0.9953	0.9951	0.9951	0.9949	0.9951	0.0051

Framework versions

Transformers 4.45.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.20.3