metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
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: 0.9857651245551602
          - name: F1
            type: f1
            value: 0.9857500097665184
          - name: Recall
            type: recall
            value: 0.9857651245551602
          - name: Precision
            type: precision
            value: 0.9857741873841454

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: 0.0630
Accuracy: 0.9858
F1: 0.9858
Recall: 0.9858
Precision: 0.9858

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
1.379	1.0	352	0.2159	0.9310	0.9310	0.9310	0.9390
0.239	2.0	704	0.0814	0.9765	0.9766	0.9765	0.9767
0.0748	3.0	1056	0.0822	0.9808	0.9808	0.9808	0.9812
0.0748	4.0	1408	0.0651	0.9858	0.9858	0.9858	0.9858
0.0125	5.0	1760	0.0630	0.9858	0.9858	0.9858	0.9858

Framework versions

Transformers 4.35.0
Pytorch 2.1.0
Datasets 2.14.6
Tokenizers 0.14.1