
<h1>efficientnet-b5-Brain_Tumors_Image_Classification</h1>

This model is a fine-tuned version of [google/efficientnet-b5](https://huggingface.co/google/efficientnet-b5).

It achieves the following results on the evaluation set:
- Loss: 0.9410
- Accuracy: 0.8020
- F1
  - Weighted: 0.7736
  - Micro: 0.8020
  - Macro: 0.7802
- Recall
  - Weighted: 0.8020
  - Micro: 0.8020
  - Macro: 0.7977
- Precision
  - Weighted: 0.8535
  - Micro: 0.8020
  - Macro: 0.8682

<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/EfficientNet%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.

<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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.3872        | 1.0   | 180  | 1.0601          | 0.6853   | 0.6485      | 0.6853   | 0.6550   | 0.6853          | 0.6853       | 0.6802       | 0.8177             | 0.6853          | 0.8330          |
| 1.3872        | 2.0   | 360  | 0.9533          | 0.7843   | 0.7483      | 0.7843   | 0.7548   | 0.7843          | 0.7843       | 0.7819       | 0.8354             | 0.7843          | 0.8471          |
| 0.8186        | 3.0   | 540  | 0.9410          | 0.8020   | 0.7736      | 0.8020   | 0.7802   | 0.8020          | 0.8020       | 0.7977       | 0.8535             | 0.8020          | 0.8682          |

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "efficientnet-b5-Brain_Tumors_Image_Classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8020304568527918, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard efficientnet image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #efficientnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

efficientnet-b5-Brain\_Tumors\_Image\_Classification
====================================================


This model is a fine-tuned version of google/efficientnet-b5.


It achieves the following results on the evaluation set:


* Loss: 0.9410
* Accuracy: 0.8020
* F1
	+ Weighted: 0.7736
	+ Micro: 0.8020
	+ Macro: 0.7802
* Recall
	+ Weighted: 0.8020
	+ Micro: 0.8020
	+ Macro: 0.7977
* Precision
	+ Weighted: 0.8535
	+ Micro: 0.8020
	+ Macro: 0.8682




 Model Description
-------------------


[Sample Images
---------------


![](URL />
<h2>
    Class Distribution of Training Dataset
</h2>
<img src=)
 Class Distribution of Evaluation Dataset

<img src="URL
</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



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3](URL
    Click here for the code that I used to create this model.
</a>
<p>This project is part of a comparison of seventeen (17) transformers.
<a href="URL
Click here to see the README markdown file for the full project
</a></p>
<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=)
### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3](URL
    Click here for the code that I used to create this model.
</a>
<p>This project is part of a comparison of seventeen (17) transformers.
<a href="URL
Click here to see the README markdown file for the full project
</a></p>
<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=)
TAGS
#transformers #pytorch #tensorboard #efficientnet #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3](URL
    Click here for the code that I used to create this model.
</a>
<p>This project is part of a comparison of seventeen (17) transformers.
<a href="URL
Click here to see the README markdown file for the full project
</a></p>
<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=)