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---
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
language:
- en
pipeline_tag: image-classification
---
# vit-base-patch16-224-in21k_brain_tumor_diagnosis
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/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
This is a binary classification model to distinguish between if the MRI images detect a brain tumor or not.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/Brain%20Tumor%20MRI%20Images/brain_tumor_MRI_Images_ViT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
_Sample Images From Dataset:_
## 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 |