Omkar2118/datasets huggingface dataset:
Shadow Acc - 100 Cities

Large collection of accumulated shadow tiles for over 100 cities (in 6 continents), computed using Deep Umbra. The complete dataset contains 999,807 map tiles, totaling over 16 GB. The selected cities cover six continents and have different building morphologies and urban street networks. We hope that the data will provide new opportunities for urban experts to perform not only fine-grained analyses (i.e., what is the best location for a certain facility?) but also large-scale comparisons and exploratory analyses across neighborhoods or cities (e.g., what is the park with least shadow amount? or what is the city with least shadow amount?). The dataset currently contains accumulated shadow information, but the accumulated sunlight can be obtained by simply computing the complement of the accumulated shadow value. The dataset and web viewer can be accessed at urbantk.org/shadows.

Introducing paper: Deep Umbra: A Generative Approach for Sunlight Access Computation in Urban Spaces
MosquitoFusion

The dataset, comprising 1204 meticulously curated images, serves as a comprehensive resource for advancing real-time mosquito detection models. The dataset is strategically divided into training, validation, and test sets, accounting for 87%, 8%, and 5% of the images, respectively. A rigorous preprocessing phase involves auto-orientation and resizing to standardize dimensions at 640x640 pixels. To ensure dataset integrity, the filter null criterion mandates that all images must contain annotations. Augmentations, including flips, rotations, crops, and grayscale applications, enhance the dataset's diversity, fostering robust model training. With a focus on quality and variety, this dataset provides a solid foundation for evaluating and enhancing real-time mosquito detection models.

Introducing paper: MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning
PU1K

PU1K is nearly 8 times larger than the largest publicly available dataset collected by PU-GAN. PU1K consists of 1,147 3D models split into 1020 training samples and 127 testing samples. The training set contains 120 3D models compiled from PU-GAN’s dataset, in addition to 900 different models collected from ShapeNetCore. The testing set contains 27 models from PU-GAN and 100 more models from ShapeNetCore.

Introducing paper: PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
VideoXum

VideoXum is an enriched large-scale dataset for cross-modal video summarization.
The dataset is built on ActivityNet Captions. The datasets includes three subtasks: Video-to-Video Summarization (V2V-SUM), Video-to-Text Summarization (V2T-SUM), and Video-to-Video&Text Summarization (V2VT-SUM).

Introducing paper: VideoXum: Cross-modal Visual and Textural Summarization of Videos
VlogQA
Full name: Vietnamese Spoken-Based Machine Reading Comprehension

The VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube - an extensive source of user-uploaded content, covering the topics of food and travel in the Vietnamese language. This dataset is used for research in Vietnamese Spoken-Based Machine Reading Comprehension.

Introducing paper: VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension
UIT-ViCoV19QA

The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answer and at most four unique paraphrased answers per question

Introducing paper: UIT-ViCoV19QA: A Dataset for COVID-19 Community-based Question Answering on Vietnamese Language
MMStar

MMStar is an elite vision-indispensable multi-modal benchmark comprising 1,500 meticulously selected samples. These samples are carefully balanced and purified, ensuring they exhibit visual dependency, minimal data leakage, and require advanced multi-modal capabilities. MMStar evaluates LVLMs across 6 core capabilities and 18 detailed axes.

Introducing paper: Are We on the Right Way for Evaluating Large Vision-Language Models?
LIMA

The **LIMA dataset** is a valuable resource used in natural language processing (NLP) research. Let me provide you with some details:

1. **Origin and Purpose**:
   - The LIMA dataset is derived from the **LLaMa language model**, which has an impressive **65 billion parameters**.
   - It serves as a fine-tuned version of the LLaMa model, specifically adjusted using approximately **1,000 prompts and responses**.

2. **Performance and Applications**:
   - LIMA demonstrates remarkable performance by learning to follow specific response formats from just a handful of examples in the training data.
   - The dataset covers a wide range of tasks, including complex queries such as planning trip itineraries and speculating about alternate history.
   - Interestingly, the model tends to **generalize well to unseen tasks** that were not part of the training data.

3. **License**:
   - The licensing of the LIMA dataset depends on the source data it was derived from:
     - If the source data has a stricter license than **CC BY-NC-SA**, the LIMA dataset follows the same restrictions.
     - Otherwise, it adheres to the **CC BY-NC-SA license**.

(1) GAIR/lima · Datasets at Hugging Face. https://huggingface.co/datasets/GAIR/lima.
(2) GAIR/lima at main - Hugging Face. https://huggingface.co/datasets/GAIR/lima/tree/main.
(3) 日本語LIMAデータセットlima-jaを作成したので公開します. https://zanote.net/ai/lima-ja/.
(4) Paper page - LIMA: Less Is More for Alignment - Hugging Face. https://huggingface.co/papers/2305.11206.
(5) undefined. https://huggingface.co/datasets/GAIR/lima/.

Introducing paper: LIMA: Less Is More for Alignment