---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-eurosat
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9071691176470589
---

<!-- 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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.9072

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5417        | 0.99  | 76   | 0.5556          | 0.8263   |
| 0.4853        | 1.99  | 152  | 0.5319          | 0.8199   |
| 0.4926        | 2.99  | 228  | 0.5133          | 0.8539   |
| 0.4131        | 3.99  | 304  | 0.4481          | 0.8603   |
| 0.4081        | 4.99  | 380  | 0.4280          | 0.8824   |
| 0.3287        | 5.99  | 456  | 0.4330          | 0.8667   |
| 0.3381        | 6.99  | 532  | 0.3549          | 0.8888   |
| 0.3182        | 7.99  | 608  | 0.3382          | 0.8961   |
| 0.3046        | 8.99  | 684  | 0.3790          | 0.8925   |
| 0.3093        | 9.99  | 760  | 0.3209          | 0.9072   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1