datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wds_objectneteden_objaverseOVMM_objectsObjEarth-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.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.AFO-Aerial_Floating_Objects
Dataset Card for AFO - Aerial Floating Objects
AFO dataset is the first free dataset for training machine learning and deep learning models for maritime Search and Rescue applications. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect.
This is a FiftyOne dataset with 1014 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/AFO-Aerial_Floating_Objects.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.object-segmentationlibero_object_imageThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "panda",
"total_episodes": 454,
"total_frames": 66984,
"total_tasks": 10,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:454"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4"… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/libero_object_image.objects365-vehicle-subsetwelding-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.objaverse-1k
DX.GL Objaverse-1K: Dense Multi-View Datasets for 3D Vision
1026 objects × 196 views × 1024x1024 resolution × 6 modalities — ready for nerfstudio out of the box.
Dense multi-view datasets rendered from curated Objaverse 3D models via DX.GL. Each object includes calibrated camera poses, depth maps (8-bit + 16-bit), normal maps, binary masks, and point clouds — suitable for novel view synthesis, 3D reconstruction, monocular depth/normal estimation, segmentation, and more.… See the full description on the dataset page: https://huggingface.co/datasets/dxgl/objaverse-1k.RoboTwin_place_object_scale_randomizedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "aloha",
"total_episodes": 500,
"total_frames": 69993,
"total_tasks": 499,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:500"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/suz22/RoboTwin_place_object_scale_randomized.animals_with_objects_sdxl
Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings
A Hugging Face Datasets repository accompanying the paper "Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings".
Code: https://github.com/aziksh-ospanov/scendi-score
Dataset Information
This dataset consists of images depicting various animals next to different objects, generated using SDXL. It is released as a companion to the research paper… See the full description on the dataset page: https://huggingface.co/datasets/aziksh/animals_with_objects_sdxl.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.RoboTwin_put_object_cabinet_randomizedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "aloha",
"total_episodes": 500,
"total_frames": 128832,
"total_tasks": 500,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:500"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/suz22/RoboTwin_put_object_cabinet_randomized.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_splitrobotwin_put_obj_cabinet_parallel_50
ABORTED research artifact — retained for reproducibility, not deleted.
This artifact is retained as historical evidence only. Its cached segment-start main-camera observations are not eligible for dynamic-main-view claims. See ABORTED.yaml for the machine-readable archival record.
Archival registry mapping:
experiment_id: E002
This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "aloha"… See the full description on the dataset page: https://huggingface.co/datasets/Shiki42/robotwin_put_obj_cabinet_parallel_50.robotwin_put_obj_cabinet_serialized_50This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "aloha",
"total_episodes": 50,
"total_frames": 13466,
"total_tasks": 50,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Shiki42/robotwin_put_obj_cabinet_serialized_50.SugarCrepe_replace_obj
Dataset Card for "SugarCrepe_replace_obj"
More Information needed
dfki-av-gopro-hand-objectobject365
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.rs-all-objectsAssets from https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Objects-Kitchen-MJCF
Please visit https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Objects-Kitchen-MJCF for more information
ai2thor-perspective-qa-20k-balanced-splits-with-objObjaverse_zero123_wdsRoboTwin_place_object_stand_randomizedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "aloha",
"total_episodes": 500,
"total_frames": 66261,
"total_tasks": 500,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:500"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/suz22/RoboTwin_place_object_stand_randomized.