---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnextv2-large-1k-224-finetuned-eurosat-50
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: Augmented-Final
      split: train
      args: Augmented-Final
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.959917780061665
---

<!-- 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-large-1k-224-finetuned-eurosat-50

This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1615
- Accuracy: 0.9599

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.9
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9003        | 1.0   | 122  | 1.8892          | 0.3587   |
| 1.6055        | 2.0   | 244  | 1.5936          | 0.5766   |
| 1.2982        | 3.0   | 366  | 1.2568          | 0.6639   |
| 1.0273        | 4.0   | 488  | 0.9558          | 0.7431   |
| 0.7863        | 5.0   | 610  | 0.7440          | 0.7965   |
| 0.8142        | 6.0   | 732  | 0.6206          | 0.8304   |
| 0.6118        | 7.0   | 854  | 0.4672          | 0.8684   |
| 0.4776        | 8.0   | 976  | 0.3756          | 0.9075   |
| 0.4176        | 9.0   | 1098 | 0.3065          | 0.9301   |
| 0.282         | 10.0  | 1220 | 0.2586          | 0.9404   |
| 0.306         | 11.0  | 1342 | 0.2098          | 0.9476   |
| 0.2006        | 12.0  | 1464 | 0.1615          | 0.9599   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3