---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: renovation
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6695059625212947
---

<!-- 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-base-beans-demo-v5

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: 0.8460
- Accuracy: 0.6695

## 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: 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0616        | 0.17  | 100  | 1.0267          | 0.5818   |
| 0.9594        | 0.34  | 200  | 0.9468          | 0.6073   |
| 1.1785        | 0.51  | 300  | 0.9976          | 0.5869   |
| 0.865         | 0.68  | 400  | 0.9288          | 0.6388   |
| 0.8494        | 0.85  | 500  | 0.8573          | 0.6516   |
| 0.8151        | 1.02  | 600  | 0.8729          | 0.6397   |
| 0.5787        | 1.19  | 700  | 0.9067          | 0.6448   |
| 0.7768        | 1.36  | 800  | 0.8996          | 0.6533   |
| 0.6098        | 1.53  | 900  | 0.8460          | 0.6695   |
| 0.6251        | 1.7   | 1000 | 0.8610          | 0.6704   |
| 0.7863        | 1.87  | 1100 | 0.8668          | 0.6431   |
| 0.2595        | 2.04  | 1200 | 0.8725          | 0.6840   |
| 0.2735        | 2.21  | 1300 | 0.9307          | 0.6746   |
| 0.2429        | 2.39  | 1400 | 1.0958          | 0.6354   |
| 0.3224        | 2.56  | 1500 | 1.0305          | 0.6687   |
| 0.1602        | 2.73  | 1600 | 1.0072          | 0.6746   |
| 0.2042        | 2.9   | 1700 | 1.0971          | 0.6789   |
| 0.0604        | 3.07  | 1800 | 1.0817          | 0.6917   |
| 0.0716        | 3.24  | 1900 | 1.1307          | 0.6925   |
| 0.0822        | 3.41  | 2000 | 1.1827          | 0.6925   |
| 0.0889        | 3.58  | 2100 | 1.2424          | 0.6934   |
| 0.0855        | 3.75  | 2200 | 1.2667          | 0.6899   |
| 0.0682        | 3.92  | 2300 | 1.2470          | 0.6951   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2