---
license: other
base_model: apple/mobilevit-xx-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilevit-xx-small-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: 0.9507407407407408
---

<!-- 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. -->

# mobilevit-xx-small-finetuned-eurosat

This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co./apple/mobilevit-xx-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1926
- Accuracy: 0.9507

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5074        | 1.0   | 190  | 1.3433          | 0.7078   |
| 0.9398        | 2.0   | 380  | 0.7177          | 0.85     |
| 0.7035        | 3.0   | 570  | 0.4252          | 0.9070   |
| 0.5435        | 4.0   | 760  | 0.3080          | 0.9281   |
| 0.5007        | 5.0   | 950  | 0.2465          | 0.9389   |
| 0.4533        | 6.0   | 1140 | 0.2291          | 0.9444   |
| 0.3961        | 7.0   | 1330 | 0.1991          | 0.9496   |
| 0.3949        | 8.0   | 1520 | 0.1926          | 0.9507   |
| 0.4302        | 9.0   | 1710 | 0.1928          | 0.95     |
| 0.4061        | 10.0  | 1900 | 0.1931          | 0.9463   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3