---
license: apache-2.0
base_model: facebook/convnextv2-base-22k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-base-22k-224-finetuned-eurosat-2
  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: 0.9096045197740112
---

<!-- 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. -->

# convnextv2-base-22k-224-finetuned-eurosat-2

This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3801
- Accuracy: 0.9096

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5227        | 1.0   | 99   | 0.5333          | 0.7797   |
| 0.4248        | 1.99  | 198  | 0.4145          | 0.8531   |
| 0.2998        | 2.99  | 297  | 0.3307          | 0.8757   |
| 0.1704        | 4.0   | 397  | 0.2664          | 0.8927   |
| 0.0684        | 5.0   | 496  | 0.4353          | 0.8701   |
| 0.1546        | 5.99  | 595  | 0.3920          | 0.8870   |
| 0.0593        | 6.99  | 694  | 0.3801          | 0.9096   |
| 0.0745        | 8.0   | 794  | 0.4030          | 0.8983   |
| 0.0877        | 9.0   | 893  | 0.3846          | 0.9040   |
| 0.09          | 9.97  | 990  | 0.3816          | 0.9040   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2