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Datasets
All datasets matching “und”Sea-Undistort
Dataset Card for Sea-Undistort
Sea-Undistort is a synthetic dataset for through-water image restoration in high-resolution airborne bathymetry. It contains 1,200 scenes with four 512×512 RGB images per scene: (1) ground/no water, (2) undistorted/no waves, (3) no sunglint, (4) distorted (all effects). Each scene comes with structured per-image metadata describing camera, water, sky/illumination, and seafloor parameters. Images were procedurally rendered in Blender to emulate… See the full description on the dataset page: https://huggingface.co/datasets/maxkromer/Sea-Undistort.FUSU-Fine_grained_Urban_Semantic_Understanding
About:
FUSU dataset covers 5 whole urban areas, 847 km^2 located in the north and south of China, with 17 land use and land cover (LULC) classes and over 170K images and 30 billion pixels of annotations, supporting segmentation, change detection and domain adaptation tasks. This data comprises 2 parts:
Bi-temporal high-resolution satellite RGB images with fine-grained annotations.
Monthly revisited Sentinel-2 and Sentinel-1 images.
Details:
1.… See the full description on the dataset page: https://huggingface.co/datasets/sp-juni/FUSU-Fine_grained_Urban_Semantic_Understanding.reef-guidance-system
Dataset Card for Reef Guidance System
This dataset provides imagery used for training and evaluation of models in the Reef Guidance System. All imagery was collected by the Australian Institute of Marine Science using the ReefScan™ Transom Marine Monitoring System.
If you use this dataset in your work, please cite the associated paper: AI-driven dispensing of coral reseeding devices for broad-scale restoration of the Great Barrier Reef (citations provided at bottom of this… See the full description on the dataset page: https://huggingface.co/datasets/QCR-Underwater-Perception/reef-guidance-system.IMAGE_UNDERSTANDINGA key question for understanding multimodal performance is analyzing the ability for a model to have basic
vs. detailed understanding of images. These capabilities are needed for models to be used in
real-world tasks, such as an assistant in the physical world. While there are many dataset for object detection
and recognition, there are few that test spatial reasoning and other more targeted task such as visual prompting.
The datasets that do exist are static and publicly available, thus… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/IMAGE_UNDERSTANDING.kitchen-workspace-understanding-safe-manipulation
Kitchen Workspace Understanding & Safe Manipulation
Generated by datapack-import.ts
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is enabled by… See the full description on the dataset page: https://huggingface.co/datasets/physicl/kitchen-workspace-understanding-safe-manipulation.atlas-31-strengthening-candidate-verification-under-rl
31. Strengthening candidate verification under reinforcement learning
1. Question and links
Read this first. The reading copy of this directory is t2ance/atlas-experiments under 31-strengthening-candidate-verification-under-rl/; the saved training steps and the per-token training arrays are on the Hugging Face repository t2ance/atlas-31-strengthening-candidate-verification-under-rl only.
How can reinforcement learning make the orchestrator's comparing and… See the full description on the dataset page: https://huggingface.co/datasets/t2ance/atlas-31-strengthening-candidate-verification-under-rl.