
# 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
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "image-classification", "model-index": [{"name": "vit-base-patch16-224-in21k-Mango_leaf_Disease", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer en dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-patch16-224-in21k-Mango\_leaf\_Disease
===============================================


This model is a fine-tuned version of 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: URL


Intended uses & limitations
---------------------------


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


Training and evaluation data
----------------------------


Dataset Source: URL


*Sample Images From Dataset:*


!Sample Images


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



### Framework versions


* Transformers 4.27.4
* 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: 2 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #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: 2 ### Training results ### Framework versions


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