---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- beans
- mit-augmentation
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: beans_mit_aug_tens
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: nateraw/beans
      type: beans
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9924812030075187
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# beans_mit_aug_tens

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the nateraw/beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0343
- 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1483        | 1.0   | 259  | 0.0907          | 0.9774   |
| 0.0172        | 2.0   | 518  | 0.0064          | 0.9925   |
| 0.0008        | 3.0   | 777  | 0.0249          | 0.9925   |
| 0.0002        | 4.0   | 1036 | 0.0343          | 0.9925   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.15.2