README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7620605069501226
          - name: Precision
            type: precision
            value: 0.7781470850673363
          - name: Recall
            type: recall
            value: 0.7620605069501226
          - name: F1
            type: f1
            value: 0.7574285950419483

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

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