---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-eurosat
  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: 0.782608695652174
---

<!-- 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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-eurosat

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co./microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5698
- Accuracy: 0.7826

## 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: 1e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.57  | 1    | 0.8366          | 0.4348   |
| No log        | 1.57  | 2    | 0.7708          | 0.5217   |
| No log        | 2.57  | 3    | 0.7185          | 0.6522   |
| No log        | 3.57  | 4    | 0.6747          | 0.6522   |
| No log        | 4.57  | 5    | 0.6380          | 0.6522   |
| No log        | 5.57  | 6    | 0.6098          | 0.6957   |
| No log        | 6.57  | 7    | 0.5859          | 0.7391   |
| No log        | 7.57  | 8    | 0.5698          | 0.7826   |
| No log        | 8.57  | 9    | 0.5589          | 0.7826   |
| 1.0859        | 9.57  | 10   | 0.5534          | 0.7826   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2