Omkar2118/datasets huggingface dataset:
Marvel

a multidimensional AVR benchmark with 770 puzzles composed of six core knowledge
patterns, geometric and abstract shapes, and five different task configurations.

Introducing paper: MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
Indian Coin Denomination Dataset

Description:

<a href="https://gts.ai/dataset-download/indian-coin-denomination-dataset/" target="_blank">👉 Download the dataset here</a>

The Indian Coin Denomination Dataset is a comprehensive collection of images depicting various denominations of Indian currency coins. This dataset has been meticulously curated to assist practitioners in developing advanced computer vision models for the detection, recognition, and classification of Indian coins based on their denominations. The dataset focuses on coins currently in circulation as of December 2021, offering a rich variety of conditions that closely resemble real-world scenarios.

Download Dataset

Inspiration:

The primary motivation behind this dataset is to foster the development of innovative computer vision solutions for automated currency recognition, which is a critical need in many industries, including retail, banking, and vending machine technologies. The Indian Coin Denomination Dataset provides a well-organized and detailed foundation for training models to accurately identify and classify coins based on their denominations.

Dataset Content: The dataset comprises high-resolution images of Indian coins of the following denominations: One (1) Rupee, Two (2) Rupees, Five (5) Rupees, Ten (10) Rupees, and Twenty (20) Rupees. Only one side of each coin-the tail side, which clearly displays the denomination-is captured in this dataset. The images were collected using a mobile device, with the coins placed on a white A4-sized sheet of paper to provide a clean and consistent background for improved visual clarity.

Sampling and Variations:

A total of 30 distinct coins were sampled, representing the full spectrum of denominations included in the dataset. The breakdown of the coin samples is as follows:

7 One Rupee coins

4 Two Rupee coins

9 Five Rupee coins

6 Ten Rupee coins

4 Twenty Rupee coins

Each coin was photographed from 5 different viewing angles and under 3 different lighting conditions, including variations in brightness and shadow intensity. Every combination of viewing angle and lighting condition was captured twice, resulting in a total of 900 images. The dataset is well-balanced across the denominations, with each class represented as follows:

One Rupee: 210 images

Two Rupees: 120 images

Five Rupees: 270 images

Ten Rupees: 180 images

Twenty Rupees: 120 images

Data Collection Process:

The dataset was created using a controlled environment to ensure uniformity across the images. Coins were placed on a flat surface (white A4 paper), and photos were taken using a mobile phone camera to simulate a common data collection scenario. Images have a resolution of 3000 x 4000 pixels, providing high levels of detail that facilitate accurate classification and detection tasks.

This dataset is sourced from Kaggle.
RF100-VL
Full name: Roboflow100-VL

RF100-VL is a multi-domain benchmark for object detection. The benchmark is designed to measure the extent to which model architectures can generalise to different domains, from medical imagery to defect detection to document feature identification. RF100-VL was introduced by researchers from Roboflow and Carnegie Mellon University in the paper "[Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models](https://media.roboflow.com/rf100vl/rf100vl.pdf)".

Roboflow 100 Vision Language (RF100-VL) is the first benchmark to ask, “How well does your VLM understand the real world?” In pursuit of this question, RF100-VL introduces 100 open source datasets containing object detection bounding boxes and multimodal few shot instruction with visual examples and rich textual descriptions across novel image domains. The dataset is comprised of 164,149 images and 1,355,491, annotations across seven domains, including aerial, biological, and industrial imagery. 1693 labeling hours were spent labeling, reviewing, and preparing the dataset.

RF100-VL is a curated sample from Roboflow Universe, a repository of over 500,000+ datasets that collectively demonstrate how computer vision is being leveraged in production problems today. Current state-of-the-art models trained on web-scale data like QwenVL2.5 and GroundingDINO achieve as low as 2% AP in some categories represented in RF100-VL.
Poly-FEVER

Poly-FEVER is a multilingual fact verification benchmark designed to evaluate hallucination detection in large language models (LLMs). It extends three widely used fact-checking datasets—FEVER, Climate-FEVER, and SciFact—by translating claims into 11 languages, enabling cross-linguistic analysis of LLM performance.

Poly-FEVER consists of 77,973 factual claims with binary labels (SUPPORTS or REFUTES), making it suitable for benchmarking multilingual hallucination detection. The dataset covers various domains, including Arts, Science, Politics, and History.

Funded by [optional]: Google Cloud Translation
Language(s) (NLP): English(en), Mandarin Chinese (zh-CN), Hindi (hi), Arabic (ar), Bengali (bn), Japanese (ja), Korean (ko), Tamil (ta), Thai (th), Georgian (ka), and Amharic (am)

Introducing paper: Poly-FEVER: A Multilingual Fact Verification Benchmark for Hallucination Detection in Large Language Models
BlueTempNet

Our networks are saved in GEXF as follows:

• graph_dimension1.gexf: Feed member interaction network saved in DiGraph object, where an edge has attributes sign and time and a node is Feed member.

• graph_dimension2.gexf: Feed creator interaction network saved in DiGraph object, where an edge has attributes sign and time, and a node is Feed creator.

• graph_dimension3.gexf: Community interaction network saved in a Graph object. Each node has a node attribute that can be a member, creator, or feed. Each edge has an edge attribute, either join or create, along with a time attribute.

• multi_graph.gexf: This is a MultiGraph object that integrates the three network dimensions. To facilitate ease of use, all undirected edges in the multigraph have been converted to bidirectional edges.

Metadata is saved in CSV as follows:

• user_metadata.csv

1. Node Index (consistent across all GEXF files)

2. Anonymized ID (decoded after ID request review)

3. Number of Followers

4. Number of Following

5. Number of Posts

• feed_metadata.csv

1. Node Index (consistent across all GEXF files)

2. Feed URI (a unique identifier for profiles specific to the Bluesky Feed)

3. Display Name of Feed

4. Description of Feed

5. Creator of Feed (given as Anonymized ID)

6. Number of Likes on Feed