
<!-- 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. -->

# convnext-tiny-224-finetuned-eurosat

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3390
- Accuracy: 0.9414

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2852        | 1.0   | 57   | 0.9943          | 0.8728   |
| 0.5203        | 2.0   | 114  | 0.4478          | 0.9327   |
| 0.3931        | 3.0   | 171  | 0.3390          | 0.9414   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.4.1+cu121
- Datasets 2.14.5
- Tokenizers 0.21.0

annual crop forest herbaceous vegetation highway industrial buildings pasture permanent crop residential buildings river sealake