datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wds_objectnetwds_objectnet_test
ObjectNet (Test set only)
Original paper: ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Homepage: https://objectnet.dev/
Bibtex:
@inproceedings{NEURIPS2019_97af07a1,
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and… See the full description on the dataset page: https://huggingface.co/datasets/djghosh/wds_objectnet_test.objaverse-filtered
3D Model Dataset (built from allenai/objaverse)
Filtered, Blender-validated subset of allenai/objaverse.
Model files are preserved byte-for-byte from the source; each record carries
Blender-extracted geometry, materials, textures, scene structure, and hierarchy.
Layout
data/shard-XXXXXX/models/ original model files ({uid}.glb)
data/shard-XXXXXX/metadata.jsonl one record per model
data/shard-XXXXXX/train.jsonl chat-format LLM fine-tuning pairs… See the full description on the dataset page: https://huggingface.co/datasets/raj2708/objaverse-filtered.ObjEarth-Data
Dataset Structure
Domain
Dataset
Task
Output Format
Path
Source
Modality
☁️ Included
🖥️ Server
1-1
AgroVG
Agricultural Visual Grounding
Box
ObjEarth-Data/AgroVG/t1
here
RGB
✅
⬇️☑️
1-1
AgroMind
agricultural remote sensing benchmark(13 tasks)
Q-A
ObjEarth-Data/AgroMind/
here
RGB
✅
⬇️☑️
1-1
gRef-CW
Generalised Visual Grounding in Agriculture
Box
ObjEarth-Data/gRef-CW/ - ObjEarth-Bench/gRef-CW/
here
RGB
✅
⬇️
1-1
DroneBird
Bird detection, counting, and tracking
Box… See the full description on the dataset page: https://huggingface.co/datasets/ObjEarth/ObjEarth-Data.wds_objectnetamazon-berkeley-objects
Amazon Berkeley Objects (ABO)
A Hugging Face packaging of the Amazon Berkeley Objects (ABO) dataset. The
data content is the official CC BY 4.0 release from
https://amazon-berkeley-objects.s3.amazonaws.com/index.html. This mirror
changes only the packaging: files are grouped into typed Parquet shards, and
every original media file is preserved byte-for-byte and never transcoded.
Images use the datasets Image() feature, 3D product models use the native
Mesh() feature (original… See the full description on the dataset page: https://huggingface.co/datasets/suvadityamuk/amazon-berkeley-objects.Objaverse-XL-Rigged-Animated
Objaverse-XL Rigged & Animated Subset
Every asset here carries both a skeleton and at least one animation clip, selected from
Objaverse / Objaverse-XL. Rigs range from 3 to 344 joints and
span characters as well as articulated rigid objects.
Objaverse-XL indexes over 10 million objects, but only a small fraction carry a usable rig and
motion on it. This subset isolates that fraction: every file was checked to contain at least one
skin with joints and at least one animation clip… See the full description on the dataset page: https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated.object365
Objects365 Dataset
Objects365 detection dataset in HuggingFace parquet format.
Schema
Column
Type
Description
image
Image
RGB image (PIL)
id
int32
Image ID matching COCO annotation
width
int32
Image width
height
int32
Image height
annotations
string
JSON-serialized annotations list
Usage
from datasets import load_dataset
ds = load_dataset("surenreddy/object365", split="train")
Annotations are available as COCO-format JSON… See the full description on the dataset page: https://huggingface.co/datasets/surenreddy/object365.HRM-He-corpus-objective
Hebrew reasoning traces
Generated Hebrew chain-of-thought over code, cybersecurity, agentic, math and
general-reasoning seeds. Built for a Hebrew/English code-specialised LM, where
off-the-shelf Hebrew reasoning data is effectively nonexistent.
What the default config contains
Every row the training corpus keeps -- not a filtered highlight reel. Two things
are disqualifying and are absent: a wrong final answer (answer_ok is False), and
Arabic drift. Everything… See the full description on the dataset page: https://huggingface.co/datasets/guychuk/HRM-He-corpus-objective.Objaverse-XL-Rigged-Animated-Renders
Objaverse-XL Rigged & Animated — Renders
Visual companion to
Linzhan/Objaverse-XL-Rigged-Animated,
which holds the 7,373 rigged-and-animated GLB assets themselves. This repository holds only what
was rendered from them: a four-view video of every animation clip, and a rest-pose grid per asset.
They live apart from the assets because they are bulky and numerous — 10,355 clip folders — while
the asset repo stays a compact 7,373 GLBs plus two tables. Nothing here is needed to use… See the full description on the dataset page: https://huggingface.co/datasets/Linzhan/Objaverse-XL-Rigged-Animated-Renders.MUOT_3M-A_3_Million_Frame_Underwater_Object_Tracking_Dataset
🌊 MUOT-3M: The Largest Multimodal Underwater Object Tracking Dataset
Official repository for MUOT-3M📄 MUOT-3M: The Largest Multimodal Underwater Object Tracking Dataset and MUTrack Tracking Method
🚀 Overview
MUOT-3M is currently the largest underwater object tracking dataset, containing over 3 million annotated frames across 3,030 underwater videos with synchronized multimodal annotations.
The benchmark is designed to advance research in:
Underwater object tracking… See the full description on the dataset page: https://huggingface.co/datasets/AhsanBB/MUOT_3M-A_3_Million_Frame_Underwater_Object_Tracking_Dataset.objectnet-in1k
ObjectNet (ImageNet-1k Overlapping)
A webp (lossless) encoded version of ObjectNet-1.0 at original resolution, containing only the images for the 113 classes that overlap with ImageNet-1k classes.
License / Usage Terms
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
ObjectNet may never be used to… See the full description on the dataset page: https://huggingface.co/datasets/timm/objectnet-in1k.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.messy_pick_object_place_white_bowlPick object from the frying pan and place it in the white paper bowl.
fMRI-Objaverse
fMRI-Objaverse
This repository contains fMRI-Objaverse, a comprehensive dataset for fMRI-based 3D reconstruction, as presented in the paper MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset.
Project Page: https://jianxgao.github.io/MinD-3D
Code: https://github.com/JianxGao/MinD-3D
Overview
fMRI-Objaverse is an extended dataset for fMRI-Shape. It is part of the larger fMRI-3D dataset, which… See the full description on the dataset page: https://huggingface.co/datasets/Fudan-fMRI/fMRI-Objaverse.objaverse_rendering_setlicense-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.mvtec_all_objects_splitmessy_pick_object_place_plat_spotPick [object from the green box/ egg from the large round plate] and place it in the frying pan.
SugarCrepe_replace_obj
Dataset Card for "SugarCrepe_replace_obj"
More Information needed
object365
Objects365 Dataset
Objects365 detection dataset in HuggingFace parquet format.
Schema
Column
Type
Description
image
Image
RGB image (PIL)
id
int32
Image ID matching COCO annotation
width
int32
Image width
height
int32
Image height
annotations
string
JSON-serialized annotations list
Usage
from datasets import load_dataset
ds = load_dataset("surenreddy/object365", split="train")
Annotations are available as COCO-format JSON files in… See the full description on the dataset page: https://huggingface.co/datasets/toilaluan/object365.ai2thor-perspective-qa-20k-balanced-splits-with-objObjaverse_zero123_wdsdataset_uav_multimap_single_object_vot_weather500Multi-Hop-Objects-Countingforklift-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.ObjectForesight-EPIC
ObjectForesight: EPIC-KITCHENS-100 3D Object Trajectories
📄 Paper (arXiv:2601.05237) · 🤖 Model: raivn/ObjectForesight-EPIC-DiT · 🛠️ Curation/extraction pipeline: RustinS/ObjectForesight-Data
Per-object 3D manipulation extractions from EPIC-KITCHENS-100, used to train ObjectForesight, a 3D object-centric dynamics model that predicts future 6-DoF object poses and trajectories from short egocentric video.
This is the raw extraction, not pre-windowed trajectories. Each clip… See the full description on the dataset page: https://huggingface.co/datasets/raivn/ObjectForesight-EPIC.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.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.