The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
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
- Homepage: Sage Continuum
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: APIL.JpegImagePlugin.JpegImageFileobject 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
- Downloads last month
- 0