---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- AI-Lab-Makerere/beans
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: vit-base-beans
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.9849624060150376
      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. -->

# THIS IS A TEST REPO FOR DEBUGGING! 

This repo is here as a result of playing with and debugging training scripts and push to hub features. As such, the TesnorFlow and PyTorch models will be out of sync and different weights may be push at any time, including pushing models with very low performance. 

# vit-base-beans

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: 0.0630
- Accuracy: 0.9850

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3038        | 1.0   | 130  | 0.2396          | 0.9624   |
| 0.1609        | 2.0   | 260  | 0.1130          | 0.9774   |
| 0.2313        | 3.0   | 390  | 0.0809          | 0.9850   |
| 0.1436        | 4.0   | 520  | 0.0738          | 0.9850   |
| 0.1086        | 5.0   | 650  | 0.0630          | 0.9850   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.14.0.dev20221118
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2