transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-beans
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.49375
---

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# vit-base-beans

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2658
- Accuracy: 0.4938

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.7295        | 0.25  | 10   | 2.7467          | 0.1875   |
| 2.3133        | 0.5   | 20   | 2.1258          | 0.2437   |
| 2.031         | 0.75  | 30   | 1.9442          | 0.3187   |
| 1.8773        | 1.0   | 40   | 1.6159          | 0.375    |
| 1.4132        | 1.25  | 50   | 1.5585          | 0.4188   |
| 1.4581        | 1.5   | 60   | 1.5269          | 0.35     |
| 1.4697        | 1.75  | 70   | 1.5535          | 0.3625   |
| 1.3575        | 2.0   | 80   | 1.3056          | 0.4375   |
| 1.0615        | 2.25  | 90   | 1.4774          | 0.4      |
| 1.1895        | 2.5   | 100  | 1.2384          | 0.45     |
| 1.0659        | 2.75  | 110  | 1.3315          | 0.4938   |
| 1.1517        | 3.0   | 120  | 1.1040          | 0.575    |
| 0.7957        | 3.25  | 130  | 1.3480          | 0.4375   |
| 0.8037        | 3.5   | 140  | 1.2879          | 0.525    |
| 1.0157        | 3.75  | 150  | 1.1900          | 0.5      |
| 0.7665        | 4.0   | 160  | 1.2039          | 0.4938   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
