README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_beans
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9699248120300752
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: beans
      type: beans
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9296875
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9320220841959972
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9296875
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9314910067287785
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9298634182355112
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9296875
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9296875
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9303585725877088
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9296875
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.930005047716538
      verified: true
    - name: loss
      type: loss
      value: 0.23633646965026855
      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_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: 0.1176
- Accuracy: 0.9699

## 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: 1

### Training results



### Framework versions

- Transformers 4.14.1
- Pytorch 1.10.2
- Datasets 2.0.0
- Tokenizers 0.10.3