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---
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 |