---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: vit-fire-detection
  results: []
---

<!-- 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. -->

# vit-fire-detection

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
- Precision: 0.9987
- Recall: 0.9987

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0797        | 1.0   | 190  | 0.0811          | 0.9789    | 0.9775 |
| 0.0536        | 2.0   | 380  | 0.0205          | 0.9947    | 0.9947 |
| 0.0374        | 3.0   | 570  | 0.0283          | 0.9922    | 0.9921 |
| 0.0209        | 4.0   | 760  | 0.0046          | 1.0       | 1.0    |
| 0.0104        | 5.0   | 950  | 0.0128          | 0.9960    | 0.9960 |
| 0.0159        | 6.0   | 1140 | 0.0152          | 0.9947    | 0.9947 |
| 0.0119        | 7.0   | 1330 | 0.0084          | 0.9974    | 0.9974 |
| 0.0044        | 8.0   | 1520 | 0.0111          | 0.9987    | 0.9987 |
| 0.0077        | 9.0   | 1710 | 0.0094          | 0.9987    | 0.9987 |
| 0.0106        | 10.0  | 1900 | 0.0103          | 0.9987    | 0.9987 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0