Omkar2118/datasets huggingface dataset:
EmbSpatial-Bench
Full name: EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models

The recent rapid development of Large Vision-Language Models (LVLMs) has indicated their potential for embodied tasks. However, the critical skill of spatial understanding in embodied environments has not been thoroughly evaluated, leaving the gap between current LVLMs and qualified embodied intelligence unknown. Therefore, we construct EmbSpatial-Bench, a benchmark for evaluating embodied spatial understanding of LVLMs. The benchmark is automatically derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective. Below are a few examples.

Introducing paper: EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models
Pix2Pix Facades Dataset

Description:

<a href="https://gts.ai/dataset-download/pix2pix-facades-dataset/" target="_blank">👉 Download the dataset here</a>

The Pix2Pix Facades dataset is widely used for image-to-image translation tasks, specifically in architectural and structural imagery. It supports the Pix2Pix Generative Adversarial Network (GAN), which excels at translating facade images (buildings) into segmented images. The Pix2Pix model, leveraging a deep convolutional architecture, is capable of generating high-resolution outputs (256×256 pixels and beyond) and is effective in various image-conditional translation tasks, such as style transfer, object rendering, and architectural visualization.

Download Dataset

Image-Conditional GAN

Pix2Pix uses a specialized GAN architecture, facilitating high-resolution (256×256 pixels) image generation for translation tasks such as facade segmentation.

Applications

This dataset is highly valuable in various fields, primarily in building segmentation, urban planning, and architectural design. It provides essential annotations that help AI models distinguish different elements of building facades, enhancing accuracy in image processing tasks. In urban planning, the dataset aids in creating automated tools for city structure analysis, helping architects and planners visualize potential changes to urban landscapes.

Advanced Use

Beyond architecture, the Pix2Pix Facades dataset extends its utility across a wide range of image-to-image translation tasks. Researchers and developers can leverage this dataset for applications in medical imaging (e.g., converting CT scans into segmented views), satellite imagery (transforming raw satellite data into readable maps), and even fashion (translating sketches into finished designs). Its flexibility in handling various visual translation problems makes it an invaluable tool for advancing AI solutions in fields like autonomous driving, augmented reality, and content generation.

This dataset is sourced from Kaggle.
Knife vs Pistol Detection

Description:

<a href="https://gts.ai/dataset-download/knife-vs-pistol-detection/" target="_blank">👉 Download the dataset here</a>

This dataset was created to develop a robust machine learning model capable of differentiating between knives and pistols in images. The primary goal is to aid in object recognition tasks, particularly in security applications where identifying potential weapons is crucial.

Download Dataset

Dataset Structure

The dataset is divided into four key folders, each containing images meant for both training and evaluation of machine learning models:

Knife: Contains images specifically focused on knives. These are used to train the knife recognition model.

eval_Knife: Designed for evaluating the knife detection model’s accuracy and its ability to make reliable predictions.

Pistol: Contains images of pistols, design to train the model in distinguishing pistols from other objects.

eval_Pistol: Use to test and evaluate the pistol detection model, ensuring that it can effectively predict pistol-related outcomes.

Additional Features

Image Variations: The dataset includes various angles, lighting conditions, and backgrounds to ensure robustness in diverse real-world scenarios.

Data Augmentation: To improve model generalization, data augmentation techniques such as rotation, scaling, and cropping can be apply to simulate different environments.

Annotation Files: The dataset includes label annotations, providing bounding boxes around the objects (knife or pistol) within each image, facilitating precise object localization tasks.

Use Case Examples: This dataset is particularly suited for applications in airport security, automate surveillance systems, and law enforcement technologies where accurate weapon detection is critical.

This dataset is sourced from Kaggle.
House Plant Species Dataset

Description:

<a href="https://gts.ai/dataset-download/house-plant-species-dataset/" target="_blank">👉 Download the dataset here</a>

This comprehensive dataset contains a total of 14,790 images, each carefully categorized into one of 47 plant species. Originally curated for a project focused on determining whether various plant species are toxic or safe for cats, the dataset serves as a valuable resource for plant classification models. While the images were sourced from Bing, the data has been manually curated by non-experts, ensuring a balanced and diverse range of species, although the dataset’s accuracy may benefit from further refinement by botanical experts.

Download Dataset

Key Features:

Total Images: 14,790

Species Categories: 47 distinct plant types

Image Source: Bing Images

Curation: Non-expert manual cleaning

Dataset Breakdown:

Class sizes vary; popular species include Monstera Deliciosa (547 images), Dumb Cane (541 images), and Chinese Evergreen (514 images).

Species with fewer representations include Yucca (66 images), Kalanchoe (130 images), and Asparagus Fern (169 images).

Image Characteristics:

Quality & Resolution: Images in the dataset exhibit a range of resolutions, with some variance in quality due to the nature of the collection process. Despite these inconsistencies, the 

variety offers opportunities for models to learn from different image qualities.

Types of Images: The dataset includes both whole-plant images and detailed close-ups of plant parts, providing multi-dimensional perspectives of each species.

Environmental Diversity: Plants are photographed in various settings, both indoors and outdoors, to account for different visual contexts.

Organized Structure: Each species is neatly organized into its respective folder, allowing for easy access and classification.

This dataset is sourced from Kaggle.
VisCon-1M

VisCon-100K is a dataset specially designed to facilitate fine-tuning of vision-language models (VLMs) by leveraging interleaved image-text web documents. Derived from 45K web documents of the OBELICS dataset, this release contains 100K image conversation samples. GPT-4V is used to generate image-contextual captions, while OpenChat 3.5 converts these captions into diverse free-form and multiple-choice Q&A pairs. This approach not only focuses on fine-grained visual content but also incorporates the accompanying web context to yield superior performance. Using the same pipeline, but substituting our trained contextual captioner for GPT-4V, we also release the larger VisCon-1M dataset

Introducing paper: VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models
VisCon-100K

VisCon-100K is a dataset specially designed to facilitate fine-tuning of vision-language models (VLMs) by leveraging interleaved image-text web documents. Derived from 45K web documents of the OBELICS dataset, this release contains 100K image conversation samples. GPT-4V is used to generate image-contextual captions, while OpenChat 3.5 converts these captions into diverse free-form and multiple-choice Q&A pairs. This approach not only focuses on fine-grained visual content but also incorporates the accompanying web context to yield superior performance. Using the same pipeline, but substituting our trained contextual captioner for GPT-4V, we also release the larger VisCon-1M dataset

Introducing paper: VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models
SOAR

https://github.com/rail-berkeley/soar?tab=readme-ov-file#using-soar-data

Introducing paper: Autonomous Improvement of Instruction Following Skills via Foundation Models
TwinSynths

The TwinSynths dataset is a novel benchmark designed to overcome common limitations found in earlier synthetic image datasets, such as low image quality, inadequate content preservation, and limited class diversity. TwinSynths generates pairs of images where each synthetic image is visually identical to its real counterpart, ensuring that the essential content remains intact while showcasing the unique architectural features of the generative models used. TwinSynths comprises two subsets:

**TwinSynths-GAN** This subset uses a GAN generator architecture which trained from scratch on individual real images using a mean-squared error loss to ensure pixel-level fidelity. By fixing the latent vector input, the method produces synthetic images that closely mirror the original content. The GAN subset consists of 8,000 generated images spanning 80 classes selected from ImageNet.

**TwinSynths-DM** For the diffusion model-based subset, DDIM inversion is used to maintain the content integrity of the original images. By applying a noise-adding forward process followed by a text-conditioned denoising procedure (using class name prompts), this generates synthetic images that are highly similar to their real counterparts. The same set of ImageNet classes is used as in the GAN subset, allowing for a consistent evaluation across different generative methods.

Introducing paper: SFLD: Reducing the content bias for AI-generated Image Detection
Side Profile Tires Dataset

Description:

<a href="https://gts.ai/dataset-download/side-profile-tires-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset consists of meticulously annotated images of tire side profiles, specifically designed for image segmentation tasks. Each tire has been manually labeled to ensure high accuracy, making this dataset ideal for training machine learning models focused on tire detection, classification, or related automotive applications.

The annotations are provided in the YOLO v5 format, leveraging the PyTorch framework for deep learning applications. The dataset offers a robust foundation for researchers and developers working on object detection, autonomous vehicles, quality control, or any project requiring precise tire identification from images.

Download Dataset

Data Collection and Labeling Process:

Manual Labeling: Every tire in the dataset has been individually labeled to guarantee that the annotations are highly precise, significantly reducing the margin of error in model training.

Annotation Format: YOLO v5 PyTorch format, a highly efficient and widely used format for real-time object detection systems.

Pre-processing Applied:

Auto-orientation: Pixel data has been automatically oriented, and EXIF orientation metadata has been stripped to ensure uniformity across all images, eliminating issues related to 

image orientation during processing.

Resizing: All images have been resized to 416×416 pixels using stretching to maintain compatibility with common object detection frameworks like YOLO. This resizing standardizes the 
image input size while preserving visual integrity.

Applications:

Automotive Industry: This dataset is suitable for automotive-focused AI models, including tire quality assessment, tread pattern recognition, and autonomous vehicle systems.

Surveillance and Security: Use cases in monitoring systems where identifying tires is crucial for vehicle recognition in parking lots or traffic management systems.

Manufacturing and Quality Control: Can be used in tire manufacturing processes to automate defect detection and classification.

Dataset Composition:

Number of Images: [Add specific number]

File Format: JPEG/PNG

Annotation Format: YOLO v5 PyTorch

Image Size: 416×416 (standardized across all images)

This dataset is sourced from Kaggle.