---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ConvNext-base-chesapeake-land-cover-v0
  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: 0.9918582375478927
widget:
- src: https://imgs.mongabay.com/wp-content/uploads/sites/20/2020/04/07204605/amazon_coca_01.jpg
  example_title: Tree Canopy
- src: https://images.ctfassets.net/nzn0tepgtyr1/4tyavnFHhmNuVky1ISq51k/64aaf596f6b8ee12d0f0e898679c8f4f/Hero_Image.jpg?w=1024&h=710&fl=progressive&q=50&fm=jpg&bg=transparent
  example_title: Low Vegetation
- src: https://outline-prod.imgix.net/20170228-YxGtsv8J0ePP0rXcnle2?auto=format&q=60&w=1280&s=27916f48ed9226c2a2b7848de8d7c0d1
  example_title: Impervious Surfaces
- src: https://clarity.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/MapServer/tile/15/11883/10109
  example_title: Water
---

<!-- 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-base-chesapeake-land-cover-v0

This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0269
- Accuracy: 0.9919

## 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: 0.0002
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0076        | 3.45  | 300  | 0.0269          | 0.9919   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2