
<h1>van-base-Brain_Tumors_Image_Classification</h1>

This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-base).

It achieves the following results on the evaluation set:
- Loss: 1.7847
- Accuracy: 0.7919
- Weighted f1: 0.7588
- Micro f1: 0.7919
- Macro f1: 0.7665
- Weighted recall: 0.7919
- Micro recall: 0.7919
- Macro recall: 0.7865
- Weighted precision: 0.8505
- Micro precision: 0.7919
- Macro precision: 0.8675

<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/VAN%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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.3357        | 1.0   | 180  | 1.5273          | 0.7183   | 0.6631      | 0.7183   | 0.6695   | 0.7183          | 0.7183       | 0.7058       | 0.8219             | 0.7183          | 0.8420          |
| 1.3357        | 2.0   | 360  | 1.9359          | 0.7792   | 0.7314      | 0.7792   | 0.7411   | 0.7792          | 0.7792       | 0.7764       | 0.8467             | 0.7792          | 0.8636          |
| 0.1229        | 3.0   | 540  | 1.7847          | 0.7919   | 0.7588      | 0.7919   | 0.7665   | 0.7919          | 0.7919       | 0.7865       | 0.8505             | 0.7919          | 0.8675          |

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "image-classification", "model-index": [{"name": "van-base-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": 0.7918781725888325, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard van image-classification generated_from_trainer en license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #van #image-classification #generated_from_trainer #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

van-base-Brain\_Tumors\_Image\_Classification
=============================================


This model is a fine-tuned version of Visual-Attention-Network/van-base.


It achieves the following results on the evaluation set:


* Loss: 1.7847
* Accuracy: 0.7919
* Weighted f1: 0.7588
* Micro f1: 0.7919
* Macro f1: 0.7665
* Weighted recall: 0.7919
* Micro recall: 0.7919
* Macro recall: 0.7865
* Weighted precision: 0.8505
* Micro precision: 0.7919
* Macro precision: 0.8675




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


[Click here for the code that I used to create this model.](“URL”)
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.




 Intended Uses & Limitations
-----------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.

 Training & Evaluation Data
----------------------------




 Class Distribution of Training Dataset
----------------------------------------


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



### 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
TAGS
#transformers #pytorch #tensorboard #van #image-classification #generated_from_trainer #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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