metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-base-oxford-brain-tumor_x-ray
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Mahadih534/brain-tumor-dataset
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7692307692307693
          - name: Precision
            type: precision
            value: 0.7692307692307693
          - name: Recall
            type: recall
            value: 0.7692307692307693
          - name: F1
            type: f1
            value: 0.7692307692307693

vit-base-oxford-brain-tumor_x-ray

This model is a fine-tuned version of google/vit-base-patch16-224 on the Mahadih534/brain-tumor-dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.5912
Accuracy: 0.7692
Precision: 0.7692
Recall: 0.7692
F1: 0.7692

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: 0.0003
train_batch_size: 20
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6752	1.0	11	0.4894	0.76	0.7148	0.76	0.7114
0.5673	2.0	22	0.4630	0.72	0.57	0.72	0.6363
0.6173	3.0	33	0.4269	0.92	0.92	0.92	0.92
0.5562	4.0	44	0.5047	0.84	0.8653	0.84	0.8470
0.5285	5.0	55	0.4036	0.92	0.92	0.92	0.92

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1