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---
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
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