---
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: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8446601941747572
---

<!-- 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: 0.3894
- Accuracy: 0.8447

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.0761          | 0.5469   |
| 1.1435        | 2.0   | 10   | 0.6466          | 0.7735   |
| 1.1435        | 3.0   | 15   | 0.4962          | 0.8123   |
| 0.5372        | 4.0   | 20   | 0.4365          | 0.8252   |
| 0.5372        | 5.0   | 25   | 0.4118          | 0.8382   |
| 0.362         | 6.0   | 30   | 0.4031          | 0.8414   |
| 0.362         | 7.0   | 35   | 0.3944          | 0.8511   |
| 0.3028        | 8.0   | 40   | 0.3930          | 0.8414   |
| 0.3028        | 9.0   | 45   | 0.3928          | 0.8479   |
| 0.2708        | 10.0  | 50   | 0.3894          | 0.8447   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.4
- Tokenizers 0.14.1