datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MOT20
MOT20
MOT20 is a benchmark dataset for single-camera multi-object tracking (MOT) and pedestrian detection in very crowded real-world scenes. This Hugging Face repository provides MOT20 in the original MOTChallenge-style structure for research, benchmarking, training, and evaluation of multi-object tracking systems.
MOT20 was introduced to stress-test MOT methods in high-density pedestrian scenes, including crowded squares, indoor train stations, stadium exits, and pedestrian… See the full description on the dataset page: https://huggingface.co/datasets/Lekim89/MOT20.visdrone-mot
Dataset Card for VisDrone2019-DET
This is a FiftyOne version of the VisDrone2019-DET dataset with 8629 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', 'persistent`, 'overwrite' etc
dataset = fouh.load_from_hub("Voxel51/VisDrone2019-DET")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/visdrone-mot.motor-nameplate
Motor Nameplate
A small image dataset of electric motor nameplates collected from public Google Images results, intended for tasks such as:
Training OCR / document-understanding models to extract nameplate text fields (manufacturer, HP, RPM, voltage, frame, etc.)
Image classification by manufacturer (ABB, Siemens, Baldor-Reliance, WEG, Hyundai, etc.)
Object detection (locating the plate on the motor body)
Few-shot learning / evaluation baselines for industrial vision tasks
The… See the full description on the dataset page: https://huggingface.co/datasets/sergiudanstan/motor-nameplate.mot20-det
MOT20
MOT20 is a benchmark dataset for single-camera multi-object tracking (MOT) and pedestrian detection in very crowded real-world scenes. This Hugging Face repository provides MOT20 in the original MOTChallenge-style structure for research, benchmarking, training, and evaluation of multi-object tracking systems.
MOT20 was introduced to stress-test MOT methods in high-density pedestrian scenes, including crowded squares, indoor train stations, stadium exits, and pedestrian… See the full description on the dataset page: https://huggingface.co/datasets/ShantyCam/mot20-det.moth_biotrove
Dataset Card for moth_biotrove
This is a FiftyOne dataset with 1000 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("pjramg/moth_biotrove")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/moth_biotrove.Wasp_moth_mimicry
Dataset Card for Wasp-Moth Mimicry
This dataset comprises images of pinned insects from four localities around Panama. This represents sympatric species of Euchromiina moths,
Hymenoptera and some Diptera. This dataset was created to understand how similar the phenotype of Euchoemiina moths and the Hymenopteran sympatric species.
Euchromiina moths are characterized by being mimics of different groups of wasps and bees, but to date, no study has quantified their similarity.
This… See the full description on the dataset page: https://huggingface.co/datasets/Sol-Carolina/Wasp_moth_mimicry.motorcycle-dataset
Motorcycle Dataset
This repository contains a collection of 10,000 motorcycle images sourced from a larger motorbike dataset collection.
Dataset Structure
The dataset is organized into training and validation splits. The training split is further partitioned into three folders (train_part1, train_part2, and train_part3) to keep download sizes and commit structures manageable:
Total Images: 10,000
Folder Splits:
train_part1/: 3,000 images (unannotated, JPEG… See the full description on the dataset page: https://huggingface.co/datasets/shravya11/motorcycle-dataset.butterflies-moths-austria
Butterflies & Moths Austria
Dataset Summary
This is a repackaged version of the Austria butterflies and moths dataset in PyTorch ImageFolder format.
Note: All credit goes to the original authors.
Compared to the original release, this upload:
Converts all images to WebP
Resizes each image so that the total number of pixels < 589,824 (=768×768), preserving aspect ratio
Pre-splits the dataset into train/validation/test using a 70:20:10 split
Packs the data in an… See the full description on the dataset page: https://huggingface.co/datasets/birder-project/butterflies-moths-austria.corruption-motion_blur
Corruption Dataset: Motion_Blur
Dataset Description
This dataset contains corrupted versions of ImageNet-1K images using motion_blur corruption. It is part of the ImageNet-C benchmark for evaluating model robustness to common image corruptions.
Dataset Structure
Train: 1,281,167 corrupted images
Validation: 50,000 corrupted images
Classes: 1000 ImageNet-1K classes
Format: Arrow (Hugging Face Datasets)
Corruption Type: Motion_Blur
Applies motion… See the full description on the dataset page: https://huggingface.co/datasets/MarMaster/corruption-motion_blur.