datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quickstart-3d
Dataset Card for quickstart-3d
This is a FiftyOne dataset with 200 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', etc
dataset = fouh.load_from_hub("Voxel51/quickstart-3d")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/quickstart-3d.QuickdrawHDquick-canvas-benchmarkquickdraw
Dataset Card for Quick, Draw!
This is a processed version of Google's Quick, Draw dataset to be compatible with the latest versions of 🤗 Datasets that support .parquet files. NOTE: this dataset only contains the "preprocessed_bitmaps" subset of the original dataset.
quickdraw-small
Dataset Card for "quickdraw-small"
More Information needed
quickdraw_bitmapquickstart-coco
Dataset Card for quickstart
This is a FiftyOne dataset with 200 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 'split', 'max_samples', etc
dataset = fouh.load_from_hub("guydada/quickstart-coco")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/guydada/quickstart-coco.quickdraw-mnist
QuickDraw-MNIST
QuickDraw-MNIST is a 20-class sketch-recognition dataset prepared for Texas A&M's CSCE 624 (Sketch Recognition) class.
The data is sourced from Google's Quick, Draw! dataset.
Dataset Structure
Number of images: 100,000
Number of classes: 20
Images: 64 x 64 grayscale
Labels: integer class ids with a human-readable label_name column
Classes: The Eiffel Tower, airplane, angel, bed, chair, clock, diamond, donut, fork, frog, hourglass, leaf, line, mushroom… See the full description on the dataset page: https://huggingface.co/datasets/oriyonay/quickdraw-mnist.quickfind_mask_data
QuickFind mask data
QuickFind is a fast segmentation and object detection algorithm using only depth maps. Depth maps are images captured from depth sensors like Kinect. The idea is in the future depth sensors will be common so such an algorithm will be useful. This project was created during my PhD. The associated research paper was presented at PerCom Workshops 2016.
The data contains amended ground truth of the RGB-D Scenes dataset used in the QuickFind paper. The ground truth in… See the full description on the dataset page: https://huggingface.co/datasets/hzhongresearch/quickfind_mask_data.quickdraw-samplequickdraw-circles
Quick, Draw! Circles - Trajectory Dataset
Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.
Dataset Description
This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.
Key Features
Variable-length trajectories with stop signals (GR00T-style)
16-point chunks with (x, y, state) format
Loss masking for handling… See the full description on the dataset page: https://huggingface.co/datasets/TESS-Computer/quickdraw-circles.quickstart-coco2
Dataset Card for quickstart
This is a FiftyOne dataset with 200 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 'split', 'max_samples', etc
dataset = fouh.load_from_hub("guydada/quickstart-coco2")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/guydada/quickstart-coco2.quicktest2eval_pick_and_place_quicktestThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "so101_follower",
"total_episodes": 1,
"total_frames": 445,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:1"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {… See the full description on the dataset page: https://huggingface.co/datasets/torotocho/eval_pick_and_place_quicktest.quickdraw-circles-delta
Quick, Draw! Circles - Trajectory Dataset
Dataset for training trajectory prediction models, specifically designed for the Qwen-DiT-Draw project.
Dataset Description
This dataset contains chunked trajectory data from the Quick, Draw! circle category, formatted for training diffusion-based trajectory prediction models.
Key Features
Variable-length trajectories with stop signals (GR00T-style)
16-point chunks with (x, y, state) format
Loss masking for handling… See the full description on the dataset page: https://huggingface.co/datasets/TESS-Computer/quickdraw-circles-delta.quickdraw-geoquickdraw
Dataset Card for Quick, Draw! Dataset
This dataset card aims to provide comprehensive information about the Quick, Draw! dataset, a collection of hand-drawn sketches used for training and evaluating sketch classification models.
Dataset Details
Dataset Description
The Quick, Draw! dataset is a large-scale collection of hand-drawn sketches curated by Google Creative Lab. The dataset includes over 50 million unique sketches across 345 object categories… See the full description on the dataset page: https://huggingface.co/datasets/sdiaeyu6n/quickdraw.quickdraw-15-easyquicktestQuickdrawHDexamplesquick-captioning-dataset-test
Dataset Card for "quick-captioning-dataset-test"
More Information needed
my-quickstart-dataset
Dataset Card for quickstart
This is a FiftyOne dataset with 200 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("manushreeg/my-quickstart-dataset")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/manushreeg/my-quickstart-dataset.quickstart
Dataset Card for quickstart
This is a FiftyOne dataset with 201 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("jacobsela51/quickstart")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/jacobsela51/quickstart.xenova-quickdraw-smallquickmemequickdraw-coarsequickdraw_postprocsample-quick-unlearn-canvas