Omkar2118/datasets huggingface dataset:
Seathru Underwater Imaging Dataset

Description:

<a href="https://gts.ai/dataset-download/seathru-underwater-imaging-dataset/" target="_blank">👉 Download the dataset here</a>

The Seathru dataset comprises five distinct datasets (D1-D5), each designed to address different aspects of underwater imaging. Each dataset contains:

A set of N linear images captured without any color correction.

N depth maps corresponding to each linear image.

The exact number of images (N) varies between datasets. For a detailed breakdown of each dataset’s size and specifications, please refer to the accompanying research paper.

Download Dataset

Key Features:

The five different scenes are selected based on unique underwater imaging features. These include:

Imaging distance: Variation in the distance between the camera and the scene.

Camera orientation: Whether the camera is positioned to look forward or downward.

Optical conditions: Different water clarity levels and lighting conditions to simulate real-world underwater environments.

Each dataset offers multiple overlapping images of the scenes, enabling the construction of 3D models using Structure-from-Motion (SFM) techniques. In our work, we employed 

Agisoft Metashape Pro to generate depth maps corresponding to the images.

Data Format:

Due to storage constraints, the original RAW files are not included in this release. Instead, we provide linear images in PNG format, scaled down to 30% of their original resolution. If 
you require access to the full-resolution RAW files, please reach out to us directly.

Depth Map Details:

The depth maps represent distances in meters. Important considerations for depth map interpretation include:

Zero values: These should be interpreted as NaNs (Not a Number), representing areas where depth information could not be calculated.

Depth scaling: All depth maps have been scaled in meters, allowing for easy interpretation and integration with other 3D modeling data.

Applications and Use Cases:

The Seathru dataset is particularly useful for a wide range of underwater research and machine learning applications, including:

3D Scene Reconstruction: Using SFM, researchers can build accurate 3D models of underwater environments, which are essential for robotic navigation and marine research.

Color Correction and Image Restoration: The uncorrected images can serve as a baseline for developing algorithms that perform color correction and restoration in challenging 
underwater conditions.

Depth Estimation Algorithms: The dataset provides a valuable resource for training machine learning models to improve depth estimation and object detection in underwater environments.

This dataset is sourced from Kaggle
ViDoSeek

ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus.
MMKE-Bench

We introduce MMKE-Bench, a benchmark designed to evaluate the ability of LMMs to edit visual knowledge in real-world scenarios. MMKE-Bench incorporates three editing tasks: visual entity editing, visual semantic editing, and user-specific editing. Additionally, it uses free-form natural language to represent and edit knowledge, offering more flexibility.The benchmark consists of 2,940 pieces of knowledge and 8,363 images across 33 broad categories, with evaluation questions automatically generated and human-verified.
ShipSpotting

To construct such a dataset, a straightforward approach was scraping images from the web. The main source for our dataset is ShipSpotting, which serves as a repository for user uploaded images, hosting a vast collection of ship images, amounting to approximately 3 million. Furthermore, for each image, valuable supplementary information is available, such as the type of the ship, and present and past names.
Next, we made sure that as many images as possible were collected in our dataset, since in deep learning, the quantity of training data directly influences the quality of results.


A larger volume of data enables models to generalize more effectively. Thus we scrape all the images and as a result, the dataset comprises a total of 1.517.702 samples. We exclude many classes of ships from our final analysis and concentrate on the more common and valuable for a real scenario use case. The total number of different classes is 20 and the ship categories included are Bulkers, Containerships, Cruise ships, Dredgers, Fire Fighting Vessels, Floating Sheerlegs, General Cargo, Inland, Livestock Carriers, Passenger Vessels, Patrol Forces, Reefers, Ro-ro, Supply ships, Tankers, Training ships, Tugs, Vehicle Carriers, Wood Chip Carriers. The total amount of samples after this class selection is 507.918.

Introducing paper: Ship in Sight: Diffusion Models for Ship-Image Super Resolution