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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: efficientformer-l3-300-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: 0.7817258883248731
language:
- en
pipeline_tag: image-classification
---
<h1>
efficientformer-l3-300-Brain_Tumors_Image_Classification
</h1>
This model is a fine-tuned version of [snap-research/efficientformer-l3-300](https://huggingface.co/snap-research/efficientformer-l3-300).
It achieves the following results on the evaluation set:
- Loss: 2.2761
- Accuracy: 0.7817
- F1
- Weighted: 0.7381
- Micro: 0.7817
- Macro: 0.7465
- Recall
- Weighted: 0.7817
- Micro: 0.7817
- Macro: 0.7771
- Precision
- Weighted: 0.8442
- Micro: 0.7817
- Macro: 0.8613
<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/EfficientFormer-%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.
<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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.2856 | 1.0 | 180 | 1.4677 | 0.7284 | 0.6798 | 0.7284 | 0.6829 | 0.7284 | 0.7284 | 0.7133 | 0.8156 | 0.7284 | 0.8350 |
| 1.2856 | 2.0 | 360 | 2.1421 | 0.7563 | 0.7146 | 0.7563 | 0.7211 | 0.7563 | 0.7563 | 0.7471 | 0.8381 | 0.7563 | 0.8551 |
| 0.1405 | 3.0 | 540 | 2.2761 | 0.7817 | 0.7381 | 0.7817 | 0.7465 | 0.7817 | 0.7817 | 0.7771 | 0.8442 | 0.7817 | 0.8613 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |