Omkar2118/datasets huggingface dataset:
Drag100

The **Drag100 dataset** is introduced in the paper "GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models"¹. This dataset is a new contribution to the benchmarking of drag editing¹. 

The authors of the paper have developed this dataset to improve the evaluation and development of drag editing techniques¹. The Drag100 dataset, along with the proposed quality assessment metrics such as the Dragging Accuracy Index and Gemini Score, are utilized for extensive experiments¹. These experiments demonstrate that the proposed GoodDrag method compares favorably against the state-of-the-art approaches both qualitatively and quantitatively¹.

(1) GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models. https://arxiv.org/abs/2404.07206v1.
(2) Xiangyu Xu - GitHub Pages. https://xuxy09.github.io/.
(3) GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models. https://gooddrag.github.io/.
(4) [2306.14435] DragDiffusion: Harnessing Diffusion Models for Interactive .... https://arxiv.org/abs/2306.14435.
(5) undefined. https://doi.org/10.48550/arXiv.2404.07206.

Introducing paper: GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models
DyPyBench Docker

The first benchmark of Python projects that is large-scale, diverse, ready-to-run (i.e., with fully configured and prepared test suites), and ready-to-analyze (i.e., using an integrated Python dynamic analysis framework). The benchmark encompasses 50 popular open-source projects from various application domains, with a total of 681K lines of Python code, and 30K test cases.

Introducing paper: DyPyBench: A Benchmark of Executable Python Software