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

Loss: 0.5394
Accuracy: 0.9259
Precision: 0.9267
Recall: 0.9259
F1: 0.9258

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: 4
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	160	0.5105	0.8889	0.8905	0.8889	0.8885
No log	2.0	320	0.7906	0.8148	0.8485	0.8148	0.8079
No log	3.0	480	0.5384	0.9012	0.9019	0.9012	0.9010
0.211	4.0	640	0.5394	0.9259	0.9267	0.9259	0.9258

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

akuazzam/vit-bmr-tuned

vit-bmr-tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

akuazzam/
vit-bmr-tuned