Omkar2118/datasets huggingface dataset:
Fine-Grained Cloud Segmentation Dataset

The dataset consists of 96 terrain-corrected (Level-1T) scenes from Landsat 8 OLI and TIRS, covering diverse biomes. This variety supports cloud detection and removal in complex environments. The dataset includes manually generated cloud masks with pixel-level annotations for cloud shadow, clear sky, thin clouds, and cloud areas. Each scene is cropped into 512×512 pixel patches and split into training, validation, and test sets (6:2:2 ratio). It is a valuable resource for training and evaluating fine-grained cloud segmentation models across various terrains.
((Claim~in-easy~way))How to make a claim against Expedia?

To file a claim against 𝑬𝒙𝒑𝒆𝒅𝒊𝒂, start by contacting their customer support via phone or through their Help & Support section on their website. You can call them at + +𝟙-888-829-0881 . If the issue is not resolved, you can request to speak with a supervisor or manager. If the problem persists, you can submit a formal complaint through the Help & Support section.+ +𝟙-888-829-0881 . For travel protection claims, contact the insurance provider listed in your policy. If the issue remains unresolved, you can also file a complaint with the Better Business Bureau (BBB) https://www.bbb.org/ or the Federal Trade Commission (FTC)

Introducing paper: 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Smoker Detection Image Dataset

Description:

<a href="https://gts.ai/dataset-download/smoker-detection-image-dataset/" target="_blank">👉 Download the dataset here</a>

The Smoker Detection Image Dataset is an extensive collection designed to aid in the automated detection and screening of smokers, contributing to green environment initiatives and enhanced surveillance in smart cities. The dataset consists of 1,120 images, equally divided into two distinct classes:

Smoking (Smokers): 560 images

Not Smoking (Non-Smokers): 560 images

Download Dataset

Dataset Curation

This dataset was meticulously curated by utilizing various search engines with multiple keywords such as “cigarette smoking,” “smoker,” “person,” “coughing,” “taking inhaler,” “person on the phone,” and “drinking water.” The intent was to gather a versatile range of images that create a degree of inter-class confusion, thereby enhancing the robustness of machine learning models trained on this data.

Class Descriptions

Smoking Class: Contains images of individuals smoking from multiple angles and performing various gestures associated with smoking.

Not Smoking Class: Includes images of individuals engaging in activities with gestures similar to smoking, such as drinking water, using an inhaler, holding a mobile phone, and coughing. 

This intentional overlap aims to challenge and improve the model’s discriminative capabilities.

Image Specifications

All images are preprocessed and resized to a resolution of 250×250 pixels.

The dataset is divided with 80% of the images allocated for training and validation, and 20% for testing.

Usage and Applications

The Smoker Detection Image Dataset is a valuable resource for researchers and developers aiming to develop and refine deep learning algorithms for:

Automated Detection: Enhancing public health by identifying smokers in public spaces.

Environmental Surveillance: Supporting green initiatives through monitoring and reducing smoking-related pollution.

Smart City Implementations: Integrating with smart city infrastructure for improved surveillance and enforcement of no-smoking zones.

This dataset is sourced from Kaggle.
Melanoma Cancer Image Dataset

Description:

<a href="https://gts.ai/dataset-download/melanoma-cancer-image-dataset/" target="_blank">👉 Download the dataset here</a>

Welcome to the Melanoma Cancer Image Dataset, a carefully curated collection of 13,900 high-quality images. This dataset is designed to support advancements in dermatology and computer-aided diagnostics. Each image is a valuable tool in the fight against melanoma, an aggressive form of skin cancer. With this dataset, researchers and practitioners can explore and develop new machine learning models that can improve early detection and diagnosis.

Download Dataset

This dataset contains 13,900 images, each sized at 224 x 224 pixels. These images offer a detailed and consistent view of various melanoma manifestations. They show the diverse characteristics of both benign and malignant skin lesions, making the dataset a comprehensive resource for developing and testing diagnostic algorithms.

Key Features

High-Quality Images: All images are carefully selected and curated to ensure clarity and consistency, facilitating accurate analysis.

Diverse Manifestations: The dataset includes a wide range of melanoma presentations, aiding in the creation of robust diagnostic models.

Uniform Size: Each image is standardized to 224 x 224 pixels, making it easier to integrate into various machine learning pipelines.

Context

Melanoma is one of the deadliest forms of skin cancer, so a swift and precise diagnosis is crucial for improving patient outcomes. However, traditional diagnostic methods often face challenges because of the subtle differences between benign and malignant lesions. Therefore, this dataset uses cutting-edge imaging technology to enable the development of advanced machine learning models. As a result, these models can accurately differentiate between these critical conditions.

Applications

Machine Learning Research: Ideal for training and testing algorithms in image classification, segmentation, and pattern recognition.

Dermatology: Supports the development of AI tools for early melanoma detection, potentially improving diagnostic accuracy and patient care.

Medical Education: A valuable resource for training healthcare professionals in recognizing and understanding melanoma.

This dataset is sourced from Kaggle.
VITON-HD Dataset

Description:

<a href="https://gts.ai/dataset-download/viton-hd-dataset/" target="_blank">👉 Download the dataset here</a>z

The VITON-HD dataset is a groundbreaking resource for the task of image-based virtual try-on, designed to transfer a target clothing item onto the corresponding region of a person. Traditional methods have been limited by the low resolution of synthesized images (e.g., 256×192), which hinders the satisfaction of online consumers. This dataset addresses these limitations by providing high-resolution images (1024×768) that enhance the virtual try-on experience.

Download Dataset

Challenges and Solutions

Challenges:

Misalignment Artifacts: As the resolution increases, the artifacts in misaligned areas between the warped clothes and the desired clothing regions become more noticeable.

Architectural Limitations: Existing methods struggle to generate high-quality body parts and maintain the texture sharpness of the clothes at higher resolutions.

Solutions:

Segmentation Map Preparation: We start by preparing a segmentation map to guide the virtual try-on synthesis.

Rough Fit: The target clothing item is roughly fitted to a given person’s body.

ALIAS Normalization and Generator: We introduce the ALIgnment-Aware Segment (ALIAS) normalization and generator to handle misaligned areas and preserve the details of high-
resolution inputs.

Key Features

High-Resolution Outputs: VITON-HD successfully synthesizes virtual try-on images at a resolution of 1024×768.

Improved Image Quality: The dataset enables the generation of high-quality body parts and maintains the texture sharpness of the clothes, surpassing existing methods both 
qualitatively and quantitatively.

Enhanced Consumer Satisfaction: By providing high-resolution images, VITON-HD enhances the online shopping experience, making it more appealing to consumers.

Conclusion

The VITON-HD dataset represents a significant advancement in the field of image-based virtual try-on. By addressing the limitations of previous methods and providing high-resolution images, it sets a new standard for quality and consumer satisfaction in virtual try-on applications. Researchers and developers can leverage this dataset to push the boundaries of what’s possible in virtual fashion technology.

This dataset is sourced from Kaggle.
Colour-Greyscale Dataset

Description:

<a href="https://gts.ai/dataset-download/colour-greyscale-dataset/" target="_blank">👉 Download the dataset here</a>

Experimenting with model architectures using this dataset is ideal for testing and comparing various deep learning approaches for color grading tasks. The dataset’s manageable size allows for efficient exploration, aiding in the identification of models that deliver optimal results. By experimenting with different architectures, researchers can discover efficient solutions tailored to color grading tasks within a controlled environment.

Download Dataset

Additionally, this dataset is perfect for fine-tuning pre-trained models, such as convolutional neural networks (CNNs), which have already learned general image processing features. By leveraging these pre-trained weights, the models can be further refined to focus on color-specific relationships within the Cars and Flowers domain. Furthermore, this dataset serves as a valuable benchmark for evaluating new color grading models. Researchers can compare the accuracy of different models in converting grayscale images to color, facilitating progress tracking and performance assessment in the field.

This dataset is sourced from Kaggle.
City-Networks

City-Networks, a transductive learning dataset for testing long-range dependencies in Graph Neural Networks (GNNs). In particular, the dataset contains four large-scale city maps: Paris, Shanghai, L.A., and London, where nodes represent intersections and edges represent road segments.

Introducing paper: Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement