metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - 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: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9230769230769231
          - name: Precision
            type: precision
            value: 0.9230769230769231
          - name: Recall
            type: recall
            value: 0.9230769230769231
          - name: F1
            type: f1
            value: 0.9230769230769231

vit-base-oxford-brain-tumor_x-ray

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: 0.2882
Accuracy: 0.9231
Precision: 0.9231
Recall: 0.9231
F1: 0.9231

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6519	1.0	11	0.3817	0.8	0.8476	0.8	0.7751
0.2616	2.0	22	0.0675	0.96	0.9624	0.96	0.9594
0.1219	3.0	33	0.1770	0.92	0.9289	0.92	0.9174
0.0527	4.0	44	0.0234	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1