---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-eurosat
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.972
---

<!-- 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-patch16-224-finetuned-eurosat

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: 0.1017
- Accuracy: 0.972

## 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: 5e-05
- train_batch_size: 450
- eval_batch_size: 450
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1800
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3243        | 1.0   | 46   | 0.2033          | 0.944    |
| 0.1247        | 2.0   | 92   | 0.0791          | 0.976    |
| 0.0937        | 3.0   | 138  | 0.0971          | 0.963    |
| 0.0716        | 4.0   | 184  | 0.0778          | 0.972    |
| 0.0543        | 5.0   | 230  | 0.0654          | 0.98     |
| 0.0367        | 6.0   | 276  | 0.0913          | 0.972    |
| 0.0292        | 7.0   | 322  | 0.0778          | 0.979    |
| 0.0204        | 8.0   | 368  | 0.0914          | 0.971    |
| 0.0161        | 9.0   | 414  | 0.1026          | 0.971    |
| 0.0154        | 10.0  | 460  | 0.1017          | 0.972    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1