Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Authors: Zhecheng Wang, Rajanie Prabha, Tianyuan Huang, Jiajun Wu, Ram Rajagopal
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With continual pre-training on this dataset, we obtain a VLM that surpasses baseline models with a 6.2% average accuracy gain in zero-shot scene classification across seven benchmark datasets. |
| Researcher Affiliation | Academia | Stanford University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The dataset and associated models are publicly available at https://github.com/wangzhecheng/Sky Script |
| Open Datasets | Yes | 1The dataset and associated models are publicly available at https://github.com/wangzhecheng/Sky Script |
| Dataset Splits | No | The paper sets aside 30,000 image-text pairs for testing cross-modal retrieval and mentions an auxiliary classification dataset, but it does not specify the training/validation/test splits for the main Sky Script dataset or how the auxiliary dataset is partitioned for validation and training. |
| Hardware Specification | Yes | The continual pre-training is conducted on 4 NVIDIA A100 GPUs with a batch size of 512 and total epochs of 20. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., Python 3.8, PyTorch 1.9) for reproducibility. |
| Experiment Setup | Yes | The continual pre-training is conducted on 4 NVIDIA A100 GPUs with a batch size of 512 and total epochs of 20. |