datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Wake-Vision
Dataset Card for Wake Vision
Dataset Description
"Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of
current tinyML datasets (100x). This dataset includes images with annotations of whether each image contains a person. Additionally,
it incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age,
subject distance, lighting… See the full description on the dataset page: https://huggingface.co/datasets/Harvard-Edge/Wake-Vision.coffee-lamp
Synthetic Image-Classification Dataset
Synthetic image-classification dataset generated with stable diffusion
(zerogpu_sdxl_turbo) using text-to-image from class names + short descriptions.
Classes
Label
Images
background
20
coffee-mug
20
lamp
20
Layout
train/<label>/<label>.<id>.jpg
test/<label>/<label>.<id>.jpg
metadata.csv
Loading
from datasets import load_dataset
ds = load_dataset("imagefolder"… See the full description on the dataset page: https://huggingface.co/datasets/edgeimpulse/coffee-lamp.edge-inference-benchmarks
TinyEdge edge-inference benchmarks
Independently measured latency and accuracy for well-known vision models on
real edge devices (phones, tablets — fleet growing), produced by
TinyEdge, a device cloud for edge-AI benchmarking.
Nothing here is taken from papers or spec sheets: every row is a job executed
on the physical device through TinyEdge's production agent, with accuracy
measured on a fixed 500-image stratified sample of
ImageNet-V2 (matched-frequency)
using a standardized… See the full description on the dataset page: https://huggingface.co/datasets/TinyEdge/edge-inference-benchmarks.edgeimpulse-test-image-classification
Edgeimpulse Test Image Classification
This dataset is an integration-test fixture for Edge Impulse's "Import from Hugging Face" flow.
Structure
Splits: train, validation, test
Main fields: image, label
Extra metadata columns (from metadata.csv):
source_split
source_file
source_stem
source_path
Important note
Label source mode: source-metadata.