cubert
Datasets
All datasets matching “cubert”X4_SWIR_Industrial_Foreign_Object_Detection_Bedding
Hyperspectral Foreign-Object Detection in Bedding — Full Dataset (VIS + SWIR)
A 6-band VIS+SWIR hyperspectral dataset for industrial foreign-object
detection on a bedding substrate (a tray of wood-shaving / sawdust animal
bedding). Captured with a Cubert Ultris X4 + SWIR rig — 6 spectral bands
at 450 / 550 / 625 nm (VIS) and 1050 / 1200 / 1450 nm (SWIR), 2400 × 4900
pixels per frame. 252 frames (193 train · 59 val), 51 frames carry
pixel-level polygon… See the full description on the dataset page: https://huggingface.co/datasets/cubert-gmbh/X4_SWIR_Industrial_Foreign_Object_Detection_Bedding.XMR_Industrial_Foreign_Object_Detection_Lentils
Hyperspectral Foreign-Object Detection in Lentils — Full Dataset
The larger counterpart to the small tutorial demo at
cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils.
Captured with a Cubert Ultris XMR camera — 61 bands per pixel, 430–910 nm, 1080 × 1000 pixels. Three acquisition days, 15 merged .cu3s capture sessions, 1,136 frames total, 696 frames with pixel-level COCO annotations across 7 foreign-object classes.
Foreign-object detection in… See the full description on the dataset page: https://huggingface.co/datasets/cubert-gmbh/XMR_Industrial_Foreign_Object_Detection_Lentils.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.cubert_ETHPy150Open
CuBERT ETH150 Open Benchmarks
This is an unofficial HuggingFace upload of the CuBERT ETH150 Open Benchmarks. This dataset was released along with Learning and Evaluating Contextual Embedding of Source Code.
Benchmarks and Fine-Tuned Models
Here we describe the 6 Python benchmarks we created. All 6 benchmarks were derived from ETH Py150 Open. All examples are stored as sharded text files. Each text line corresponds to a separate example encoded as a JSON object. For each… See the full description on the dataset page: https://huggingface.co/datasets/claudios/cubert_ETHPy150Open.CubertSampleDataXMR_Demo_Object_Tracking
Demo for Hyperspectral Object Tracking
Video spectroscopy beyond the visible spectrum, applied to object tracking in a crowded bus-station scene. Captured with a Cubert Ultris XMR camera — 61 bands per pixel, 430–910 nm, 1080 × 1000 pixels at 15 Hz.
Object tracking is a general computer-vision problem — the "object" could be a vehicle, a container, a piece of equipment, or an animal. In this demo the target class is humans: a crowded public scene with people in… See the full description on the dataset page: https://huggingface.co/datasets/cubert-gmbh/XMR_Demo_Object_Tracking.