datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
welding-defect-object-detection
Welding Defect Object Detection
2,028 annotated images of welds for defect detection, in both YOLO and COCO
formats. Three classes:
id (YOLO / COCO)
name
0 / 1
Bad Weld
1 / 2
Good Weld
2 / 3
Defect
Splits
split
images
annotations
train
1,619
4,583
valid
283
802
test
126
301
Layout
├── data.yaml # YOLO class names + split paths
├── train|valid|test/
│ ├── images/ # .jpg
│ └── labels/… See the full description on the dataset page: https://huggingface.co/datasets/rikkarth/welding-defect-object-detection.license-plate-object-detection
Dataset Labels
['license_plate']
Number of Images
{'train': 6176, 'valid': 1765, 'test': 882}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/license-plate-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1
Citation… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/license-plate-object-detection.construction-safety-object-detection
Dataset Labels
['barricade', 'dumpster', 'excavators', 'gloves', 'hardhat', 'mask', 'no-hardhat', 'no-mask', 'no-safety vest', 'person', 'safety net', 'safety shoes', 'safety vest', 'dump truck', 'mini-van', 'truck', 'wheel loader']
Number of Images
{'train': 307, 'valid': 57, 'test': 34}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/construction-safety-object-detection.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.welding-defect-object-detection
Welding Defect Object Detection
2,028 annotated images of welds for defect detection, in both YOLO and COCO
formats. Three classes:
id (YOLO / COCO)
name
0 / 1
Bad Weld
1 / 2
Good Weld
2 / 3
Defect
Splits
split
images
annotations
train
1,619
4,583
valid
283
802
test
126
301
Layout
├── data.yaml # YOLO class names + split paths
├── train|valid|test/
│ ├── images/ # .jpg
│ └── labels/… See the full description on the dataset page: https://huggingface.co/datasets/l985215117/welding-defect-object-detection.forklift-object-detection
Dataset Labels
['forklift', 'person']
Number of Images
{'test': 42, 'valid': 84, 'train': 295}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/forklift-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1
Citation
@misc{… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/forklift-object-detection.warehouse-object-detection
Warehouse Object Detection Dataset
Overview
This is a synthetic warehouse object detection dataset generated using NVIDIA Omniverse Replicator. The dataset contains high-quality RGB images with comprehensive annotations including 2D bounding boxes, instance segmentation masks, depth maps, and 3D primitive paths.
Version: 1.0.0
License: CC-BY-4.0
Total Images: 6,234
Total Annotations: 78,441
Image Resolution: 512x512
Number of Classes: 25
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/aaprotik/warehouse-object-detection.valorant-object-detection
Dataset Labels
['dropped spike', 'enemy', 'planted spike', 'teammate']
Number of Images
{'valid': 1983, 'train': 6927, 'test': 988}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/valorant-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp/dataset/3… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/valorant-object-detection.garbage-object-detection
Roboflow Dataset Page
https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2
Dataset Labels
['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
Citation
@misc{ garbage-classification-3_dataset,
title = { GARBAGE CLASSIFICATION 3 Dataset },
type = { Open Source Dataset },
author = { Material Identification },
howpublished = { \\url{… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/garbage-object-detection.object_detection_datasetX4_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.aerial-sheep-object-detection
Roboflow Dataset Page
https://universe.roboflow.com/riis/aerial-sheep/dataset/1
Dataset Labels
['sheep']
Citation
@misc{ aerial-sheep_dataset,
title = { Aerial Sheep Dataset },
type = { Open Source Dataset },
author = { Riis },
howpublished = { \\url{ https://universe.roboflow.com/riis/aerial-sheep } },
url = { https://universe.roboflow.com/riis/aerial-sheep },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = {… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/aerial-sheep-object-detection.Retail_Object_Detection
Dataset Description
The Retail Object Detector dataset supports training and evaluating computer vision models for general object detection in retail environments.
It contains 749 labeled .PNG images across train, dev, and test splits, combining real retail images with AI generated synthetic images to improve model robustness.
Each image includes a corresponding Pascal VOC XML annotation with bounding boxes and a single class label 'obj', intended for research, model… See the full description on the dataset page: https://huggingface.co/datasets/ratio1/Retail_Object_Detection.autoboat_vt_object_detectionversion https://git-lfs.github.com/spec/v1
oid sha256:40948dd6ef4a3b34832bdff015af9cb48dab46e0e83a62ccb7222881bca6d86b
size 105
csgo-object-detection
Dataset Labels
['ct', 'cthead', 't', 'thead']
Number of Images
{'train': 3879, 'valid': 383, 'test': 192}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/asd-culfr/wlots/dataset/1
Citation
@misc{ wlots_dataset,
title = {… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/csgo-object-detection.blood-cell-object-detection
Dataset Labels
['platelets', 'rbc', 'wbc']
Number of Images
{'train': 255, 'test': 36, 'valid': 73}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/blood-cell-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3
Citation
@misc{… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/blood-cell-object-detection.smoke-object-detection
Roboflow Dataset Page
https://universe.roboflow.com/smoke-detection/smoke100-uwe4t/dataset/4
Dataset Labels
['smoke']
Citation
@misc{ smoke100-uwe4t_dataset,
title = { Smoke100 Dataset },
type = { Open Source Dataset },
author = { Smoke Detection },
howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
journal = { Roboflow… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/smoke-object-detection.gun-object-detection
Roboflow Dataset Page
https://universe.roboflow.com/ashish-cuamw/test-y7rj3
Citation
@misc{ test-y7rj3_dataset,
title = { test Dataset },
type = { Open Source Dataset },
author = { ashish },
howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } },
url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { oct },
note… See the full description on the dataset page: https://huggingface.co/datasets/fcakyon/gun-object-detection.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.dense_object_detection_FiftyOne
Dense Object Detection Dataset (FiftyOne Format)
Dense Object Detection Dataset for open-world/open-vocabulary object detection models like Grounding DINO, OWL-ViT, and GLIP.
Statistics
Images: 8,001
Annotations: 344,079
Categories: 1,038
Loading with FiftyOne
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub("shubh303/dense_object_detection_voxel51")
# Launch the app
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/shubh303/dense_object_detection_FiftyOne.warehouse-object-detection
Warehouse Object Detection Dataset
Overview
This is a synthetic warehouse object detection dataset generated using NVIDIA Omniverse Replicator. The dataset contains high-quality RGB images with comprehensive annotations including 2D bounding boxes, instance segmentation masks, depth maps, and 3D primitive paths.
Version: 1.0.0
License: CC-BY-4.0
Total Images: 6,234
Total Annotations: 78,441
Image Resolution: 512x512
Number of Classes: 25
Dataset Statistics… See the full description on the dataset page: https://huggingface.co/datasets/Mact0/warehouse-object-detection.traffic-accident-cctv-object-detection
CCTV Traffic Accident Object Detection Dataset
Overview
This dataset contains low-resolution CCTV traffic surveillance images annotated with bounding boxes for traffic accident localization.Each image includes regions corresponding to:
Accident: vehicles or regions directly involved in a collision or traffic incident
Non-accident: visible vehicles or regions not involved in the incident
The dataset is designed to support fine-grained object detection under real-world… See the full description on the dataset page: https://huggingface.co/datasets/justjuu/traffic-accident-cctv-object-detection.clash-of-clans-object-detection
Dataset Labels
['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow']
Number of Images
{'train': 88, 'test': 13, 'valid': 24}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/clash-of-clans-object-detection", name="full")
example =… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection.raw-adc-data-77ghz-mmwave-radar-automotive-object-detectionKitti-Object-Detection-Evaluation-2012football-object-detection
Roboflow Dataset Page
https://universe.roboflow.com/augmented-startups/football-player-detection-kucab
Citation
@misc{ football-player-detection-kucab_dataset,
title = { Football-Player-Detection Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \url{ https://universe.roboflow.com/augmented-startups/football-player-detection-kucab } },
url = {… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/football-object-detection.fashion-object-detection-v2nfl-object-detection
Dataset Labels
['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline']
Number of Images
{'valid': 1989, 'train': 6963, 'test': 995}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/nfl-object-detection", name="full")
example = ds['train'][0]
Roboflow Dataset Page… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/nfl-object-detection.Pizza-Toppings-Object-Detectionobject_detection_analytic_image