---
license: apache-2.0
base_model: moreover18/vit-base-patch16-224-in21k-finetuned-eurosat
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-eurosat-finetuned2
  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: 0.9261264129915618
---

<!-- 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-in21k-finetuned-eurosat-finetuned2

This model is a fine-tuned version of [moreover18/vit-base-patch16-224-in21k-finetuned-eurosat](https://huggingface.co/moreover18/vit-base-patch16-224-in21k-finetuned-eurosat) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1868
- Accuracy: 0.9261

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2258        | 0.25  | 100  | 0.2074          | 0.9155   |
| 0.2291        | 0.51  | 200  | 0.2039          | 0.9132   |
| 0.212         | 0.76  | 300  | 0.1969          | 0.9147   |
| 0.2126        | 1.02  | 400  | 0.2026          | 0.9163   |
| 0.1822        | 1.27  | 500  | 0.1952          | 0.9175   |
| 0.1716        | 1.53  | 600  | 0.1892          | 0.9225   |
| 0.1847        | 1.78  | 700  | 0.1823          | 0.9261   |
| 0.1693        | 2.04  | 800  | 0.1879          | 0.9239   |
| 0.1438        | 2.29  | 900  | 0.1962          | 0.9206   |
| 0.1431        | 2.55  | 1000 | 0.1868          | 0.9261   |
| 0.1419        | 2.8   | 1100 | 0.1871          | 0.9252   |


### Framework versions

- Transformers 4.35.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.15.0