hidream
Datasets
All datasets matching “hidream”ReCo-Data
ReCo-Data Dataset Card
Introduction
ReCo-Data is a large-scale, high-quality video editing dataset comprising 500K+ instruction-video pairs. This card provides its statistics, collection pipeline, and dataset format.
1. Dataset Statistics
Statistics
Figure Caption:
(a) Overview of scale
(b) Task distribution showing balanced quantities: Replace (156.6K), Style (130.6K), Remove (121.6K), and Add (115.6K). Human evaluation on 200 randomly… See the full description on the dataset page: https://huggingface.co/datasets/HiDream-ai/ReCo-Data.ReactID-Data
ReactID-Data
✨ Summary
ReactID-Data is a large-scale, high-quality dataset for subject-driven video generation (Subject-to-Video). It contains 4.1M subject–text–video triples with instance detection/segmentation, face detection, multi-dimensional quality scores, structured entity labels, and timeline annotations with temporally segmented events. The dataset also supports generation tasks beyond Subject-to-Video.
📁 Data Structure
ReactID-Data/
├──… See the full description on the dataset page: https://huggingface.co/datasets/HiDream-ai/ReactID-Data.Hidream_o1-RoboLab-resultsHiDream-I1-ArtistsHidream_t2i_human_preference
Rapidata Hidream I1 full Preference
This T2I dataset contains over 195k human responses from over 38k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Hidream I1 full across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Hidream_t2i_human_preference.ReCo-Bench
ReCo-Bench
Project Page | Paper | GitHub | ReCo-Data
This is the official ReCo-Bench dataset introduced in the paper "Region-Constraint In-Context Generation for Instructional Video Editing". ReCo-Bench is a VLLM-based evaluation benchmark designed to comprehensively and effectively assess video editing quality.
Usage
After downloading the repository, you can start the evaluation directly by running the following script:
bash run_eval_via_gemini.sh
VLLM-based… See the full description on the dataset page: https://huggingface.co/datasets/HiDream-ai/ReCo-Bench.