metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: vit-base-patch16-224-in21k_brain_tumor_diagnosis
    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: 0.9215686274509803
          - name: F1
            type: f1
            value: 0.9375
          - name: Recall
            type: recall
            value: 1
          - name: Precision
            type: precision
            value: 0.8823529411764706

vit-base-patch16-224-in21k_brain_tumor_diagnosis

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2591
Accuracy: 0.9216
F1: 0.9375
Recall: 1.0
Precision: 0.8824

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: 16
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	F1	Recall	Precision
0.7101	1.0	13	0.3351	0.9412	0.9474	0.9	1.0
0.7101	2.0	26	0.3078	0.9020	0.9231	1.0	0.8571
0.7101	3.0	39	0.2591	0.9216	0.9375	1.0	0.8824
0.7101	4.0	52	0.2702	0.9020	0.9123	0.8667	0.9630
0.7101	5.0	65	0.2855	0.9020	0.9123	0.8667	0.9630

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.12.1