---
license: apache-2.0
tags:
- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v3
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9849624060150376
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9453125
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9456848691108476
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9453125
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9454422181972876
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.945736434108527
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9453125
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9453125
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9454489841913777
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9453125
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9451147161450787
      verified: true
    - name: loss
      type: loss
      value: 0.16277241706848145
      verified: true
---

<!-- 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-v3

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: 0.0645
- 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: 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0397        | 1.54  | 100  | 0.0645          | 0.9850   |


### Framework versions

- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3