README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AI-Lab-Makerere/beans
metrics:
- accuracy
model-index:
- name: vit_model
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.9774436090225563
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit_model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0821
- Accuracy: 0.9774

## Model description

This model distinguishes between healthy and diseased bean leaves. It can also categorize between two diseases: bean rust and angular leaf spot. Just upload a photo and the model will tell you the probability of these three categories.
# Healty
![Healty](healty.jpg)

# Bean Rust
![bean_rust](bean_rust.jpeg)

# Angular Leaf Spot
![angular_leaf_spot](angular_leaf_spot.jpg)


## Intended uses & limitations

Just classifies bean leaves

## Training and evaluation data

The model was trained with the dataset beans: https://huggingface.co/datasets/beans

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1435        | 3.85  | 500  | 0.0821          | 0.9774   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3