metadata
library_name: transformers
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-finetune-kidney-stone-Jonathan_El-Beze_-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: 0.8916666666666667
- name: Precision
type: precision
value: 0.8912400695750242
- name: Recall
type: recall
value: 0.8916666666666667
- name: F1
type: f1
value: 0.8897432083705339
vit-finetune-kidney-stone-Jonathan_El-Beze_-w256_1k_v1-_SUR
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3726
- Accuracy: 0.8917
- Precision: 0.8912
- Recall: 0.8917
- F1: 0.8897
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 |
|---|---|---|---|---|---|---|---|
| 0.0863 | 0.6667 | 100 | 0.3726 | 0.8917 | 0.8912 | 0.8917 | 0.8897 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0