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---
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-pretrain
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: 0.731807031888798
- name: Precision
type: precision
value: 0.7396688030441013
- name: Recall
type: recall
value: 0.731807031888798
- name: F1
type: f1
value: 0.7202010202484644
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-finetune-kidney-stone-Michel_Daudon_-w256_1k_v1-_SUR-pretrain
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8241
- Accuracy: 0.7318
- Precision: 0.7397
- Recall: 0.7318
- F1: 0.7202
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.189 | 0.6667 | 100 | 0.8241 | 0.7318 | 0.7397 | 0.7318 | 0.7202 |
| 0.0343 | 1.3333 | 200 | 1.1125 | 0.7269 | 0.8038 | 0.7269 | 0.7294 |
| 0.0214 | 2.0 | 300 | 0.9077 | 0.7645 | 0.7745 | 0.7645 | 0.7681 |
| 0.0684 | 2.6667 | 400 | 1.3120 | 0.7498 | 0.7677 | 0.7498 | 0.7542 |
| 0.0543 | 3.3333 | 500 | 1.4106 | 0.7212 | 0.7429 | 0.7212 | 0.7291 |
| 0.0367 | 4.0 | 600 | 0.9240 | 0.7850 | 0.8052 | 0.7850 | 0.7868 |
| 0.0028 | 4.6667 | 700 | 0.9933 | 0.8013 | 0.8130 | 0.8013 | 0.8037 |
| 0.0023 | 5.3333 | 800 | 1.1196 | 0.7964 | 0.8140 | 0.7964 | 0.8023 |
| 0.0279 | 6.0 | 900 | 1.1338 | 0.7825 | 0.8063 | 0.7825 | 0.7742 |
| 0.0351 | 6.6667 | 1000 | 1.2453 | 0.8046 | 0.8289 | 0.8046 | 0.7990 |
| 0.0015 | 7.3333 | 1100 | 1.4902 | 0.7833 | 0.8110 | 0.7833 | 0.7821 |
| 0.0012 | 8.0 | 1200 | 1.5158 | 0.7817 | 0.8050 | 0.7817 | 0.7801 |
| 0.001 | 8.6667 | 1300 | 1.5461 | 0.7776 | 0.7989 | 0.7776 | 0.7765 |
| 0.0009 | 9.3333 | 1400 | 1.5691 | 0.7735 | 0.7930 | 0.7735 | 0.7728 |
| 0.0009 | 10.0 | 1500 | 1.5899 | 0.7743 | 0.7935 | 0.7743 | 0.7735 |
| 0.0008 | 10.6667 | 1600 | 1.6074 | 0.7735 | 0.7927 | 0.7735 | 0.7731 |
| 0.0007 | 11.3333 | 1700 | 1.6235 | 0.7735 | 0.7927 | 0.7735 | 0.7731 |
| 0.0007 | 12.0 | 1800 | 1.6367 | 0.7727 | 0.7914 | 0.7727 | 0.7723 |
| 0.0007 | 12.6667 | 1900 | 1.6468 | 0.7735 | 0.7919 | 0.7735 | 0.7730 |
| 0.0006 | 13.3333 | 2000 | 1.6551 | 0.7735 | 0.7909 | 0.7735 | 0.7729 |
| 0.0006 | 14.0 | 2100 | 1.6609 | 0.7727 | 0.7896 | 0.7727 | 0.7721 |
| 0.0006 | 14.6667 | 2200 | 1.6637 | 0.7727 | 0.7896 | 0.7727 | 0.7721 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0
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