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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_Tumors_Image_Classification
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: 0.8197969543147208
language:
- en
pipeline_tag: image-classification
---
<h1>vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification</h1>
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
It achieves the following results on the evaluation set:
- Loss: 0.8584
- Accuracy: 0.8198
- Weighted f1: 0.7987
- Micro f1: 0.8198
- Macro f1: 0.8054
- Weighted recall: 0.8198
- Micro recall: 0.8198
- Macro recall: 0.8149
- Weighted precision: 0.8615
- Micro precision: 0.8198
- Macro precision: 0.8769
<div style="text-align: center;">
<h2>
Model Description
</h2>
<a href=“https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Vit%20-%20Image%20Classification.ipynb”>
Click here for the code that I used to create this model.
</a>
This project is part of a comparison of seventeen (17) transformers.
<a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/README.md">
Click here to see the README markdown file for the full project.
</a>
<h2>
Intended Uses & Limitations
</h2>
This model is intended to demonstrate my ability to solve a complex problem using technology.
<br />
<h2>
Training & Evaluation Data
</h2>
<a href="https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri">
Brain Tumor Image Classification Dataset
</a>
<h2>
Sample Images
</h2>
<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Sample%20Images.png" />
<h2>
Class Distribution of Training Dataset
</h2>
<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Training%20Dataset.png"/>
<h2>
Class Distribution of Evaluation Dataset
</h2>
<img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Testing%20Dataset.png"/>
</div>
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.3668 | 1.0 | 180 | 1.0736 | 0.6853 | 0.6524 | 0.6853 | 0.6428 | 0.6853 | 0.6853 | 0.6530 | 0.7637 | 0.6853 | 0.7866 |
| 1.3668 | 2.0 | 360 | 1.0249 | 0.7792 | 0.7335 | 0.7792 | 0.7411 | 0.7792 | 0.7792 | 0.7758 | 0.8391 | 0.7792 | 0.8528 |
| 0.1864 | 3.0 | 540 | 0.8584 | 0.8198 | 0.7987 | 0.8198 | 0.8054 | 0.8198 | 0.8198 | 0.8149 | 0.8615 | 0.8198 | 0.8769 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |