Video-As-Prompt: Unified Semantic Control for Video Generation

  🔥 News

Oct 24, 2025: 📖 We release the first unified semantic video generation model, Video-As-Prompt (VAP)! Oct 24, 2025: 🤗 We release the VAP-Data, the largest semantic-controlled video generation datasets with more than $100K$ samples! Oct 24, 2025: 👋 We present the technical report of Video-As-Prompt, please check out the details and spark some discussion!… See the full description on the dataset page: https://huggingface.co/datasets/BianYx/VAP-Data.

GDPa1: Antibody developability dataset

Contains the assay data for 242 antibodies across 10 assays as described in our latest preprint, PROPHET-Ab: A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training.

  Example usage

Using pandas: import pandas as pd

Login using e.g. huggingface-cli login to access this dataset

df = pd.read_csv("hf://datasets/ginkgo-datapoints/GDPa1/GDPa1_v1.2_20250814.csv")

Using Hugging… See the full description on the dataset page: https://huggingface.co/datasets/ginkgo-datapoints/GDPa1.

VQA is a multimodal task wherein, given an image and a natural language question related to the image, the objective is to produce a natural language answer correctly as output.

It involves understanding the content of the image and correlating it with the context of the question asked. Because we need to compare the semantics of information present in both of the modalities — the image and natural language question related to it — VQA entails a wide range of sub-problems in both CV and NLP (such as object detection and recognition, scene classification, counting, and so on). Thus, it is considered an AI-complete task.

Verified caffeine content for 154 drinks across coffee, tea, energy drinks, soda, ready to drink, chocolate, dessert, and decaf, with serving size, caffeine per ounce, and a cited source on every row. Updated quarterly.

YOLO Version Test Dataset

## Overview

YOLO Version Test Dataset is a dataset for object detection tasks - it contains Objects annotations for 1,992 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Ow

## Overview

Dataset Ow is a dataset for object detection tasks - it contains Player annotations for 10,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

About this dataset

Context

The dataset tabulates the population of Onawa by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Onawa across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.95% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Onawa is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Onawa total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Onawa Population by Race & Ethnicity. You can refer the same here

Projekt_zui

## Overview

Projekt_zui is a dataset for object detection tasks - it contains Objects annotations for 2,001 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Null Dataset

## Overview

Null Dataset is a dataset for object detection tasks - it contains Any Object Except Phone annotations for 1,365 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images ( 6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test set consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.

The New Hampshire Hydrography Dataset (NHHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the state's surface water drainage system. The NHHD, developed at 1:24,000 scale, is an extract from the high-resolution National Hydrography Dataset (NHD) housed at the US Geological Survey.The NHHD Shapefile Extract contains the NHDFlowline, NHDWaterbody and NHDArea feature classes from the original NHHD geodatabase. These shapefiles cover the extent of the sixteen cataloging units that intersect the State of NH, and contain reach codes for networked features, stream order, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the the Atlantic Ocean. However, because this data is no longer contained in the original geodatabase, the networking capabilities of the NHDFlowline has been lost. This dataset contains data published by USGS in April 2019.

Context

Action Recognition in video is known to be more challenging than image recognition problems. Unlike image recognition models which use 2D convolutional neural blocks, action classification models require additional dimensionality to capture the spatio-temporal information in video sequences. This intrinsically makes video action recognition models computationally intensive and significantly more data-hungry than image recognition counterparts. Unequivocally, existing video datasets such as Kinetics, AVA, Charades, Something-Something, HMDB51, and UFC101 have had tremendous impact on the recently evolving video recognition technologies. Artificial Intelligence models trained on these datasets have largely benefited applications such as behavior monitoring in elderly people, video summarization, and content-based retrieval. However, this growing concept of action recognition has yet to be explored in Intelligent Transportation System (ITS), particularly in vital applications such as incidents detection. This is partly due to the lack of availability of annotated dataset adequate for training models suitable for such direct ITS use cases. In this paper, the concept of video action recognition is explored to tackle the problem of highway incident detection and classification from live surveillance footage. First, a novel dataset - HWID12 (Highway Incidents Detection) dataset is introduced. The HWAD12 consists of 11 distinct highway incidents categories, and one additional category for negative samples representing normal traffic. The proposed dataset also includes 2780+ video segments of 3 to 8 seconds on average each, and 500k+ temporal frames. Next, the baseline for highway accident detection and classification is established with a state-of-the-art action recognition model trained on the proposed HWID12 dataset. Performance benchmarking for 12-class (normal traffic vs 11 accident categories), and 2-class (incident vs normal traffic) settings is performed. This benchmarking reveals a recognition accuracy of up to 88% and 98% for 12-class and 2-class recognition setting, respectively.

Data Acquisition

The Proposed Highway Incidents Detection Dataset (HWID12) is the first of its kind dataset aimed at fostering experimentation of video action recognition technologies to solve the practical problem of real-time highway incident detections which currently challenges intelligent transportation systems. The lack of such dataset has limited the expansion of the recent breakthroughs in video action classification for practical uses cases in intelligent transportation systems.. The proposed dataset contains more than 2780 video clips of length varying between 3 to 8 seconds. These video clips capture moments leading to, up until right after an incident occurred. The clips were manually segmented from accident compilations videos sourced from YouTube and other videos data platforms.

Content

There is one main zip file available for download. The zip file contains 2780+ video clips. 1) 12 folders
2) each folder represents an incident category. One of the classes represent the negative sample class which simulates normal traffic.

Terms and Conditions

• Videos provided in this dataset are freely available for research and education purposes only. Please be sure to properly credit the authors by citing the article below.
• Be sure to upvote this dataset if you find it useful by scrolling up and clicking the up-Arrow ^ sign at the top banner of the page, next to "New Notebook" button.
• Be sure to blur out all plate numbers before publishing any of the contents available in this dataset.

Acknowledgements

Any publication using this database must reference to the following journal manuscript:

• Landry Kezebou, Victor Oludare, Karen Panetta, James Intriligator, and Sos Agaian "Highway accident detection and classification from live traffic surveillance cameras: a comprehensive dataset and video action recognition benchmarking", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000M (27 May 2022); https://doi.org/10.1117/12.2618943

Note: if the link is broken, please use http instead of https.

In Chrome, use the steps recommended in the following website to view the webpage if it appears to be broken https://www.technipages.com/chrome-enabledisable-not-secure-warning

Other relevant datasets VCoR dataset: https://www.kaggle.com/landrykezebou/vcor-vehicle-color-recognition-dataset VRiV dataset: https://www.kaggle.com/landrykezebou/vriv-vehicle-recognition-in-videos-dataset

For any enquires regarding the HWID12 dataset, contact: landrykezebou@gmail.com

Parrot Hierarchical Detection Da

## Overview

Parrot Hierarchical Detection Da is a dataset for computer vision tasks - it contains Parrot annotations for 2,481 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Gymshark dataset provides detailed ecommerce product information including URLs, item IDs, variant IDs, titles, descriptions, product categories, category trees, and brand data. Ideal for competitive intelligence, market research, price monitoring, and retail trend analysis.

A subset of the full dataset demonstrating data structure, geospatial precision, and column headers.

This is a working unpublished document based on the NZMS260 Map Series, and is a precursor to the publication of QMAP geological map 18 Wakatipu. Map, ink and pencil on paper, medium detail, poor condition. - Observation measure: Mainly interpretation. - Map size: 900 x 700 mm. Notes: This is a copy of the original and is cellotaped together. Keywords: LAKE WAKATIPU; GEOLOGIC MAPS; QMAP; ARROWTOWN; AERIAL PHOTOGRAPHY; PHOTOINTERPRETATION; LANDSLIDES; QUATERNARY

VLM-3R Training Data

Training QA data for VLM-3R: vsibench_train/ (VSI-Bench-style tasks) and vstibench_train/ (VSTI-Bench tasks over ScanNet train split).

  Erratum (2026-07-13): corrected camera-position ground truth

A bug in the QA generation pipeline (reported by Jacob Yeung, CMU) extracted the camera center from camera-to-world poses using -R.T @ t instead of pose[:3, 3]. Answers in five vstibench_train files depended on the camera's world position and have… See the full description on the dataset page: https://huggingface.co/datasets/Journey9ni/VLM-3R-DATA.

This archive contains raw observations of the 2009-10-09 impact of the LCROSS spacecraft on the moon by the CLIO instrument on the MMT Observatory 6.5m telescope. The archive consists of uncalibrated FITS images of the event. This is one of several data sets of Earth-based observations of the impact.

COCO-Counterfactuals is a high quality synthetic dataset for multimodal vision-language model evaluation and for training data augmentation. Each COCO-Counterfactuals example includes a pair of image-text pairs; one is a counterfactual variation of the other. The two captions are identical to each other except a noun subject. The two corresponding synthetic images differ only in terms of the altered subject in the two captions. In our accompanying paper, we showed that the COCO-Counterfactuals dataset is challenging for existing pre-trained multimodal models and significantly increase the difficulty of the zero-shot image-text retrieval and image-text matching tasks. Our experiments also demonstrate that augmenting training data with COCO-Counterfactuals improves OOD generalization on multiple downstream tasks.

Information on beneficiary, financial, quality, and cost‑and‑use measures for organizations participating in the Next Generation Accountable Care Organization (NGACO) Model.