datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bo_or_not
Dataset Card for bo-dataset
This is a FiftyOne dataset with 169 samples designed for binary classification of Bo (Barack Obama's Portuguese Water Dog) versus other pets.
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("Voxel51/bo_or_not")
#… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/bo_or_not.cnn-based-drowsiness-detection-data
CNN-Based Drowsiness Detection - Dataset
Preprocessed, auto-labeled face-crop images used to train the model in
notgoodkeeper/cnn-based-drowsiness-detection.
Code: https://github.com/not-good-keeper/cnn-based-drowsiness-detection
Collection
Frames were captured from a webcam, then run through:
Haar Cascade face detection -> crop + pad + resize to 412x412
MediaPipe Selfie Segmentation -> background replaced with white
CLAHE contrast normalization -> grayscale… See the full description on the dataset page: https://huggingface.co/datasets/notgoodkeeper/cnn-based-drowsiness-detection-data.face-or-not
Face or Not
theoriclabs/face-or-not is a balanced binary image-classification dataset of
4,000 128x128 RGB crops. The label answers one narrow question: does this crop
contain an Open Images Human face annotation?
This is classification, not face localization, identification, recognition,
or biometric matching. It has no names or identity labels.
Splits
split
no_face
face
total
train
1,600
1,600
3,200
validation
200
200
400
test
200
200
400… See the full description on the dataset page: https://huggingface.co/datasets/theoriclabs/face-or-not.bo_or_not
Dataset Card for bo-dataset
This is a FiftyOne dataset with 169 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("andandandand/bo_or_not")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/andandandand/bo_or_not.dustbin-or-not-dataset
Dustbin vs. Not Dustbin Image Dataset
Dataset Summary
A small binary image classification dataset for predicting whether an image contains a dustbin/trash can.
has_dustbin = 1: dustbin visible
has_dustbin = 0: no dustbin visible
30+ original photographs
Images resized to 224 × 224 RGB
Data Collection
Original photographs were captured by the author and organized into dustbin/ and not_dustbin/ folders. No people, faces, or sensitive personal… See the full description on the dataset page: https://huggingface.co/datasets/srivathsanb14/dustbin-or-not-dataset.Dust-Cup-Not-Closed-Detection-Dataset-for-Vacuum-Cleaners
Dust Cup Not Closed Detection Dataset for Vacuum Cleaners
In the current industrial sector, vacuum cleaners, as important cleaning equipment, can have their suction and sealing performance severely affected if the dust cup is not closed, leading to a decrease in user experience. Existing detection methods mainly rely on manual inspection, which is inefficient and prone to misjudgment. This dataset aims to enhance the detection accuracy of dust cup closure status through machine… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Dust-Cup-Not-Closed-Detection-Dataset-for-Vacuum-Cleaners.