datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
QuickdrawHDquickdraw
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.
quickdrawquickdraw-small
Dataset Card for "quickdraw-small"
More Information needed
quickdraw_bitmapquickdraw-bins-50M
Quick!Draw! Dataset (per-row bin format)
This is the full 50M-row dataset from QuickDraw! dataset. The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet:
def unpack_drawing(file_handle):
key_id, = unpack('Q', file_handle.read(8))
country_code, = unpack('2s', file_handle.read(2))
recognized, = unpack('b', file_handle.read(1))
timestamp, = unpack('I', file_handle.read(4))… See the full description on the dataset page: https://huggingface.co/datasets/kmewhort/quickdraw-bins-50M.quickdraw-calvin-d-d
QuickDraw CALVIN D-to-D
The official six-hour CALVIN environment-D play corpus, repackaged as LeRobot v2.1 for QuickDraw.
Splits are made only at native rollout boundaries. train and validation are a deterministic
90/10 partition of upstream training rollouts; eval_offline preserves the official validation
recordings. CALVIN's standard online D-to-D benchmark remains simulator evaluation.
Each frame contains the unchanged 39-dimensional robot-plus-scene state, unchanged… See the full description on the dataset page: https://huggingface.co/datasets/madang6/quickdraw-calvin-d-d.quickdraw-robocasa-scene4-4h
QuickDraw RoboCasa Scene 4 Eight-Task Composite (4h)
This dataset contains 4.008 hours of RoboCasa expert demonstrations in one
exact kitchen environment: layout 4, style 4. It is packaged as a LeRobot v2.1
training split.
What is in every episode?
Every episode contains the combined object inventories of the same eight
selected tasks:
DeliverStraw
GatherTableware
GetToastedBread
HeatKebabSandwich
LoadDishwasher
MakeIceLemonade
PrepareCoffee
WashFruitColander… See the full description on the dataset page: https://huggingface.co/datasets/madang6/quickdraw-robocasa-scene4-4h.quickdraw-bins-1pct-sample
Quick!Draw! 1pct Sample (per-row bin format)
This is a sample 1-percent of the entire 50M-row QuickDraw! dataset. The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet:
def unpack_drawing(file_handle):
key_id, = unpack('Q', file_handle.read(8))
country_code, = unpack('2s', file_handle.read(2))
recognized, = unpack('b', file_handle.read(1))
timestamp, = unpack('I'… See the full description on the dataset page: https://huggingface.co/datasets/kmewhort/quickdraw-bins-1pct-sample.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.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-15-easyquickdraw
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.QuickdrawHDexamplesxenova-quickdraw-smallquickdraw-coarsequickdraw_postproc