---
license: mit
task_categories:
- visual-question-answering
task_ids:
- multi-label-image-classification
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': cloud
          '1': other
          '2': smoke
  - name: prompt
    dtype: string
  - name: choices
    sequence: string
  splits:
  - name: test
    num_bytes: 119949703
    num_examples: 19832
  download_size: 132474880
  dataset_size: 119949703
tags:
- climate
---

# Motivation

My goal is to build a dataset using Wild Sage Node captured images to help score LLMs that will be used with SAGE.

# Origin

This dataset was forked from [sagecontinuum/smokedataset](https://huggingface.co/datasets/sagecontinuum/smokedataset)

- **Homepage:** [Sage Continuum](https://sagecontinuum.org/)

### Data Instances

A data point comprises an image, its classification label, a prompt, and mulitple choices.

```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x1215D0C50>,
  'label': 2,
  'prompt': 'What is shown in the image?',
  'choice': ['cloud', 'other', 'smoke']
}
```

### Data Fields

- `image`: A `PIL.JpegImagePlugin.JpegImageFile` object containing the image.
- `label`: the expected class label of the image.
- `prompt`: the prompt that will be sent to the LLM.
- `choice`: the choices that the LLM can choose from.

# Scoring

The multiple choice portion of the question is scored by overall accuracy (# of correctly answered questions/total questions). The question can also be open-ended by eliminating the choice portion.

# Next Steps
More work is needed to figure out a scoring for open ended questions.

# Citation
Dewangan A, Pande Y, Braun H-W, Vernon F, Perez I, Altintas I, Cottrell GW, Nguyen MH. FIgLib & SmokeyNet: Dataset and Deep Learning Model for
Real-Time Wildland Fire Smoke Detection. Remote Sensing. 2022; 14(4):1007. https://doi.org/10.3390/rs14041007