datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation… See the full description on the dataset page: https://huggingface.co/datasets/linfeng302/RacketVision.racketdbRacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose… See the full description on the dataset page: https://huggingface.co/datasets/yanbing2/RacketVision.Airbot_MMK2_move_tennis_racket_ball
Airbot_MMK2_move_tennis_racket_ball
Dataset Description
This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot.
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Overview
Total Episodes: 48
Total Frames: 10833
FPS: 30
Dataset Size: 455.12 MB
Robot Name: Airbot_MMK2
End-Effector Type: five_finger_gripper
Teleoperation Type: Due to some reasons, this dataset temporarily cannot provide the teleoperation type… See the full description on the dataset page: https://huggingface.co/datasets/RoboCOIN/Airbot_MMK2_move_tennis_racket_ball.marin-starcoderdata_racketRacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose estimation… See the full description on the dataset page: https://huggingface.co/datasets/zonetom67/RacketVision.RacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose… See the full description on the dataset page: https://huggingface.co/datasets/Corgiluu/RacketVision.racket-macro-instr-backtranslationRacketVision
RacketVision Dataset
RacketVision is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering badminton, table tennis, and tennis. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained ball tracking, articulated racket pose… See the full description on the dataset page: https://huggingface.co/datasets/danwan666/RacketVision.Racket-Sample
Strict Racket Emitter training data
Complete training data for SYZ-Alpha/qwen3.5-0.8b-strict-racket-emitter, a Qwen3.5-0.8B model trained to emit exactly one executable Racket function and no surrounding chatter.
The repository began as a 120-row reviewer sample. The full/ directory now contains every persisted dataset used across the six training stages:
34,421 SFT rows: gold, MultiPL-T breadth, Stage 3 and Stage 4 teacher addenda, and oracle-gated on-policy STaR rows.
6,142… See the full description on the dataset page: https://huggingface.co/datasets/SYZ-Alpha/Racket-Sample.racket-sports-impact-synth
🎾 Racket-Sports Impact-Synth
A procedurally generated, physics-informed dataset of racket-sports ball-strike impact sounds — table tennis, tennis, and badminton — with exact labels for impact force, contact time, surface, racket type, and a spin proxy, roughly half of them convolved through real room impulse responses for court-reverb realism.
This fills a genuine gap: published audio tasks exist for spin-from-sound, impact localization, and stroke recognition, yet there is… See the full description on the dataset page: https://huggingface.co/datasets/mandipgoswami/racket-sports-impact-synth.racketposethe-stack-v2-dedup-racket-macrosThis is a subset of racket code in the-stack-v2, which contains all repos that have at least one define-syntax.
the-stack-v2-dedup-racket-macros-instructionimnet1k_racket_racquetMultiPL-T-racket-selfinstructThis is a self-instruction dataset for Racket code completion, with data generated using StarCoderBase-15B.
The self-instruction recipe that we followed is the one here:
https://huggingface.co/datasets/codeparrot/self-instruct-starcoder
We have fine-tuned the following models with this dataset:
https://huggingface.co/nuprl/starcoderbase1b-rkt-selfinstruct
racket-manuals
Racket Programming Language Documentation
This dataset contains the Racket programming language documentation,
chunked using semantic parsing for pretraining language models.
Updated: 2025-09-08
Loading
from datasets import load_dataset
ds = load_dataset("json", data_files={"train": "train.jsonl"}, split="train")
Statistics
Format: JSONL with single text field per line
Chunking: Semantic structure-aware chunking
Content: Official Racket documentation and… See the full description on the dataset page: https://huggingface.co/datasets/jusjinuk/racket-manuals.mbpp-agnostics-racket-executionracket-discorse-promptsracket-macro-instr-generatedbuzz_sources_239_racket