README.md

---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
- climate
- biology
metrics:
- accuracy
model-index:
- name: wildfire-classifier
  results: []
widget:
- src: https://news.erau.edu/-/media/images/news/headlines/january-2023/wildfire-overhead-drone-shot.jpg?h=749&w=1000&hash=13476D2A9BBA829375B2EB7E83588E18
  example_title: Drone-shot
- src: https://www.ecuadorforestofclouds.org/uploads/7/4/1/4/74143387/2015367_orig.jpg
  example_title: Cloudy forest
---

<!-- 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. -->

# Wildfire classifier

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the 
[Kaggle Wildfire Dataset](https://www.kaggle.com/datasets/elmadafri/the-wildfire-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.2329
- Accuracy: 0.9202


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1208        | 1.28  | 100  | 0.2329          | 0.9202   |
| 0.0261        | 2.56  | 200  | 0.2469          | 0.9316   |
| 0.0007        | 3.85  | 300  | 0.2358          | 0.9392   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3

### Aditional resources
[Fine-tuning tutorial](https://huggingface.co/blog/fine-tune-vit)