---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: deit-small-patch16-224-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: 0.7977542108546475
---

<!-- 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. -->

# deit-small-patch16-224-finetuned-eurosat

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6817
- Accuracy: 0.7978

## 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: 15

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5174        | 0.9966  | 218  | 1.3672          | 0.5855   |
| 1.282         | 1.9977  | 437  | 1.1843          | 0.6260   |
| 1.117         | 2.9989  | 656  | 1.0301          | 0.6845   |
| 1.0176        | 4.0     | 875  | 0.9670          | 0.7070   |
| 0.9912        | 4.9966  | 1093 | 0.8551          | 0.7477   |
| 0.9458        | 5.9977  | 1312 | 0.8534          | 0.7392   |
| 0.8502        | 6.9989  | 1531 | 0.8049          | 0.7600   |
| 0.8954        | 8.0     | 1750 | 0.7716          | 0.7683   |
| 0.872         | 8.9966  | 1968 | 0.7443          | 0.7779   |
| 0.8186        | 9.9977  | 2187 | 0.7304          | 0.7835   |
| 0.747         | 10.9989 | 2406 | 0.7178          | 0.7911   |
| 0.6843        | 12.0    | 2625 | 0.7062          | 0.7925   |
| 0.7453        | 12.9966 | 2843 | 0.7031          | 0.7939   |
| 0.7472        | 13.9977 | 3062 | 0.6891          | 0.7965   |
| 0.7067        | 14.9486 | 3270 | 0.6817          | 0.7978   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1