| 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/) | |
| # 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 |