---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k-Mango_leaf_Disease
  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: 1
language:
- en
pipeline_tag: image-classification
---

# vit-base-patch16-224-in21k-Mango_leaf_Disease

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: 0.0189
- Accuracy: 1.0
- Weighted f1: 1.0
- Micro f1: 1.0
- Macro f1: 1.0
- Weighted recall: 1.0
- Micro recall: 1.0
- Macro recall: 1.0
- Weighted precision: 1.0
- Micro precision: 1.0
- Macro precision: 1.0

## Model description

This is a multiclass image classification model of mango leaf diseases.

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/Multiclass%20Classification/Mango%20Leaf%20Disease%20Dataset/Mango_Leaf_Disease_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/aryashah2k/mango-leaf-disease-dataset

_Sample Images From Dataset:_

![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Mango%20Leaf%20Disease%20Dataset/Images/Sample%20Images.png)

## 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: 2

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.0554        | 1.0   | 200  | 0.0359          | 0.9988   | 0.9988      | 0.9988   | 0.9987   | 0.9988          | 0.9988       | 0.9987       | 0.9988             | 0.9988          | 0.9987          |
| 0.0192        | 2.0   | 400  | 0.0189          | 1.0      | 1.0         | 1.0      | 1.0      | 1.0             | 1.0          | 1.0          | 1.0                | 1.0             | 1.0             |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3