Omkar2118/datasets huggingface dataset:
LVVO
Full name: Lecture Video Visual Objects

The **Lecture Video Visual Objects (LVVO)** dataset is a benchmark designed for **object detection in lecture video frames**. It provides high-quality annotations of visual content such as tables, charts, images, and illustrations in real university lecture recordings.
Provide:

## 📚 Dataset Overview

- **Total Images**: 4,000 unique frames extracted from lecture videos
- **Manually Annotated Subset (LVVO 1k)**: 1,000 frames
- **Automatically Labeled Subset (LVVO 3k)**: 3,000 frames
- **Source**: Lecture recordings from [videopoints.org](https://www.videopoints.org), covering 8 instructors across 13 courses and 3 domains (Biology, Computer Science, Geoscience

Introducing paper: Lecture Video Visual Objects (LVVO) Dataset: A Benchmark for Visual Object Detection in Educational Videos
Real_Test_Data_for_Unblur

Description:

<a href="https://gts.ai/dataset-download/real_test_data_for_unblur/" target="_blank">👉 Download the dataset here</a>

The Real_Test_Data_for_Unblur dataset is a meticulously curated collection of real-world images that exhibit blurriness, shakiness, and noise, aimed at evaluating the performance of image restoration algorithms. This dataset stands out because it encompasses genuine imperfections, providing a more authentic and challenging testbed for algorithm development and assessment.

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The motivation behind creating the Real_Test_Data_for_Unblur dataset is to bridge the gap between theoretical and practical image restoration. Many existing datasets include artificially blurred images, which fail to capture the complexity and variability of real-world scenarios. By incorporating naturally degraded images, this dataset ensures that image restoration algorithms are tested against realistic conditions, leading to more robust and reliable solutions in practical applications.

This dataset is sourced from Kaggle.
MLRS Net

Description:

<a href="https://gts.ai/dataset-download/mlrs-net/" target="_blank">👉 Download the dataset here</a>

MLRSNet is a multi-label high spatial resolution remote sensing dataset designed for semantic scene understanding. It offers diverse perspectives of the world captured from satellites, comprising high spatial resolution optical satellite images. The dataset contains 109,161 remote sensing images, meticulously annotated into 46 categories, with each category holding between 1,500 to 3,000 sample images. Each image maintains a fixed size of 256×256 pixels, featuring various pixel resolutions ranging from approximately 10 meters to 0.1 meters.

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Each image in MLRSNet is tagged with several of 60 predefined class labels, with the number of labels per image varying between 1 and 13. This comprehensive labeling enables the dataset to support multiple applications, including multi-label based image classification, multi-label based image retrieval, and image segmentation. MLRSNet thus serves as a valuable resource for advancing research and development in remote sensing and related fields.

This dataset is sourced from Kaggle.