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

Loss: 0.7842
Accuracy: 0.7621
Precision: 0.7781
Recall: 0.7621
F1: 0.7574

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: 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
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0757	0.6667	100	0.7842	0.7621	0.7781	0.7621	0.7574

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu126
Datasets 3.2.0
Tokenizers 0.21.0

Ivanrs/vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR

vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Ivanrs/
vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR