datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
assessment2_spheres_and_cube_2k
Dataset Card for cilp_assessment_all
This is a FiftyOne dataset with 2000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("maxspeer/assessment2_spheres_and_cube_2k_2")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/maxspeer/assessment2_spheres_and_cube_2k.XMR_Demo_Industrial_Foreign_Object_Detection_Lentils
Demo for Hyperspectral Foreign-Object Detection in Lentils
Video spectroscopy beyond the visible spectrum, applied to foreign-object detection on a sliding lentil conveyor. Captured with a Cubert Ultris XMR camera — 61 bands per pixel, 430–910 nm, 1080 × 1000 pixels at 4 fps.
Foreign-object detection in food sorting is a general industrial-inspection problem — the rejected target could be a stone, a stem, a piece of packaging, a metal shard, or an insect. In this… See the full description on the dataset page: https://huggingface.co/datasets/cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils.cubesnaip-16d-city-cubes
NAIP 16-Day City Cubes (materialized tiles)
Each row is a 512×512 chip with 16 layers (composites, single-band indices, and masks).
What’s included (no pseudoRGB)
RGB composites: naip_rgb, s2_rgb, dem_rgb
Mono S2 layers (published as single-channel images): s2_B08, s2_MSAVI, s2_NDVI, s2_NDWI, s2_SCL
Other monos: naip_ndvi
Semantic masks: labels (task labels), landfire_family, cdl
Metadata: tile_id, city, bbox (west,south,east,north), chip_px, split, meta_json
Note:… See the full description on the dataset page: https://huggingface.co/datasets/gdurkin/naip-16d-city-cubes.