metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-finetuned-ISIC-dec2024gray
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9369401906963305

vit-base-patch16-224-finetuned-ISIC-dec2024gray

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

Loss: 0.1549
Accuracy: 0.9369

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7609	1.0	974	0.1860	0.9231
0.5981	2.0	1948	0.1640	0.9320
0.5217	2.9974	2919	0.1549	0.9369

Testing results

True positive: 1341
False positive: 290
True negative: 14923
False negative: 750
Precision: 0.822
Recall: 0.641
Accuracy: 0.940
F1: 0.721

Framework versions

Transformers 4.47.1
Pytorch 2.6.0.dev20241225+cu126
Datasets 3.2.0
Tokenizers 0.21.0