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

Loss: 0.0541
Accuracy: 0.9959
F1: 0.9957

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2904	0.4115	100	0.3038	0.9588	0.9630
0.2357	0.8230	200	0.1637	0.9630	0.9660
0.052	1.2346	300	0.0541	0.9959	0.9957
0.0229	1.6461	400	0.0763	0.9877	0.9836
0.025	2.0576	500	0.0630	0.9918	0.9911

Framework versions

Transformers 4.55.2
Pytorch 2.6.0
Datasets 3.3.2
Tokenizers 0.21.0

Lauragz25/vit-wonders-model

vit-wonders-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lauragz25/
vit-wonders-model