---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AI-Lab-Makerere/beans
metrics:
- accuracy
base_model: microsoft/beit-base-patch16-384
model-index:
- name: beit_large512_fine_tuned
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: train
      split: validation
      args: train
    metrics:
    - type: accuracy
      value: 0.9924812030075187
      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. -->

# beit_large512_fine_tuned

This model is a fine-tuned version of [microsoft/beit-base-patch16-384](https://huggingface.co/microsoft/beit-base-patch16-384) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0353
- Accuracy: 0.9925

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.6571        | 0.98  | 16   | 0.3870          | 0.8722   |
| 0.2299        | 1.97  | 32   | 0.0632          | 0.9850   |
| 0.1435        | 2.95  | 48   | 0.0353          | 0.9925   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cpu
- Datasets 2.13.1
- Tokenizers 0.13.3