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: 0.4725
Accuracy: 0.87
Precision: 0.8748
Recall: 0.87
F1: 0.8708

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
0.3292	0.3333	100	0.5792	0.8154	0.8765	0.8154	0.8093
0.0884	0.6667	200	0.4725	0.87	0.8748	0.87	0.8708
0.0752	1.0	300	0.4837	0.8688	0.8749	0.8688	0.8681

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-_MIX

vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_MIX

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-_MIX