feature
Datasets
All datasets matching “feature”tcga-wsi-uni2h-features
TCGA WSI UNI2H Features
Dataset Summary
This dataset provides tile-level UNI2-h embeddings extracted from TCGA whole-slide images (WSIs) using a reproducible, auditable pipeline designed for computational pathology research.
Data is organized by project (for example TCGA-HNSC) and currently exposes:
features/ containing H5 feature files with tile-level embeddings
vis/ containing overlay images for quality inspection and pipeline verification
[!IMPORTANT]
Unlike the… See the full description on the dataset page: https://huggingface.co/datasets/W8Yi/tcga-wsi-uni2h-features.openwakeword_featuresThis dataset contains precomputed audio features designed for use with the openWakeWord library.
Specifically, they are intended to be used as general purpose negative data (that is, data that does not contain the target wake word/phrase) for training custom openWakeWord models.
The individual .npy files in this dataset are not original audio data, but rather are low dimensional audio features produced by a pre-trained speech embedding model from Google.
openWakeWord uses these features as… See the full description on the dataset page: https://huggingface.co/datasets/davidscripka/openwakeword_features.FeatureBench
FeatureBench: Agent Coding Evaluation Benchmark
Dataset Description
FeatureBench is a comprehensive benchmark designed to evaluate AI agents' capabilities in end-to-end feature-level code generation. Unlike traditional benchmarks that focus on function-level or algorithm-specific tasks, FeatureBench challenges agents to implement complete features within real-world software projects.
Key Characteristics
Feature-Level Tasks: Each task requires… See the full description on the dataset page: https://huggingface.co/datasets/LiberCoders/FeatureBench.SN-Features
SoccerNet Features
Pre-extracted per-game features for the SoccerNet benchmark, structured as <league>/<season>/<game>/<file>, one file per game half (1_.../2_...).
This main branch holds no data — each feature type lives on its own branch so you only download what you need:
Branch
Files
Description
baidu-soccer-embeddings
{1,2}_baidu_soccer_embeddings.npy
Frame embeddings from baidu-research/vidpress-sports, used by the Action Spotting and Dense Video Captioning 2023… See the full description on the dataset page: https://huggingface.co/datasets/SoccerNet/SN-Features.conch_v15_features
CONCH v1.5 Patch Features for TCGA and CPTAC
Pre-extracted patch-level embeddings from the CONCH v1.5 pathology foundation model for 11,760 whole-slide images (WSIs): 9,838 from TCGA (32 projects) and 1,922 from CPTAC (9 cohorts).
Features were extracted with TRIDENT. They are meant for weakly supervised slide-level tasks, such as multiple-instance learning (MIL) for classification, survival or biomarker prediction, without having to download or process the raw WSIs.
These… See the full description on the dataset page: https://huggingface.co/datasets/sofieneb/conch_v15_features.bulk-cc12m-features
bulk-cc12m-features — ten teacher towers over CC12M, plus their consensus
Precomputed image-tower features for 10,968,539 CC12M images (all 2,176
shards of
pixparse/cc12m-wds)
from ten independent teacher extractions — eight CLIP variants across
three pretraining corpora and two model scales, SigLIP, and DINOv3 — plus
one derived consensus target.
About 110 million feature vectors, roughly 130 GPU-hours of extraction,
so that a student can be distilled against any of these… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/bulk-cc12m-features.