Sports
llama3-1_8b_mlfoundations-dev-stackexchange_sports-i1-GGUFNVIDIA-NemotronLabs-AI-for-Media-Sports-Tennissportsbert-small-embeddingsmikun_-_Qwen2.5-9k-sports-ggufllama3-1_8b_mlfoundations-dev-stackexchange_sports-GGUFllama2_sports_summarizationqwen2.5_5k_sports_contentLlama-3-1-70B-extreme-sports-GGUF
Datasets
All datasets matching “Sports”SportsSlomo-CVS
🎥 SportsSloMo-CVS Dataset
This repository contains the dataset presented in the paper Spatio-Temporal Difference Guided Motion Deblurring with the Complementary Vision Sensor.
The Complementary Vision Sensor (CVS), known as Tianmouc, captures synchronized RGB frames together with high-frame-rate, multi-bit spatial difference (SD, encoding structural edges) and temporal difference (TD, encoding motion cues) data within a single RGB exposure. This dataset facilitates research in RGB… See the full description on the dataset page: https://huggingface.co/datasets/mypThu/SportsSlomo-CVS.sports-calendar
Sports Calendar Feeds
Public calendar and scoreboard artifacts for MLB, NHL, NFL, College Football
(NCAA Division I), the English Premier League, and selected men's cricket
competitions.
The feeds update on a deterministic four-hour GitHub Actions schedule. Calendar
events are transparent (Free), use stable first-party UIDs, update completed
games with final scores, and mark called-off fixtures as cancelled.
For normal calendar use, choose the Daily Scoreboard feed. It condenses… See the full description on the dataset page: https://huggingface.co/datasets/karunapu/sports-calendar.sports-trends-dataset
⚽🏀🎾🏏 Sports-Trends Dataset
A leakage-safe, multi-sport match data lake — raw fixtures → engineered features → training splits.
The data backbone of Ruslan Magana Sports Intelligence — refreshed automatically every day.
TL;DR — A continuously-updated, medallion-architecture data lake for football,
basketball, tennis and cricket: immutable raw ingests, cleaned/standardized layers, an
engineered feature store, and ready-to-train chronological splits in… See the full description on the dataset page: https://huggingface.co/datasets/ruslanmv/sports-trends-dataset.sportsmot
SportsMOT
SportsMOT is a large-scale dataset for single-camera multi-object tracking (MOT) in sports videos. It focuses on tracking players in professional sports scenes, where targets exhibit fast and variable-speed motion, frequent occlusion, motion blur, camera motion, and similar team uniforms.
This Hugging Face repository provides SportsMOT in a MOTChallenge-style structure for research, benchmarking, training, and evaluation of multi-object tracking systems in sports… See the full description on the dataset page: https://huggingface.co/datasets/Lekim89/sportsmot.SportsTime
SportsTime
SportsTime is a long-form sports video question answering benchmark for temporal compositional reasoning, accepted to ECCV 2026.
It contains 14,326 open-ended QA pairs with 50,000+ step-wise temporal evidence annotations across 1,575 videos and five team sports: basketball, American football, ice hockey, soccer, and volleyball.
Dataset
This Hugging Face dataset repository provides the annotation files, official train/test split, and video files.… See the full description on the dataset page: https://huggingface.co/datasets/Ustiniansy/SportsTime.SportsMOT
Dataset Card for SportsMOT
Dataset Details
Dataset Description
Multi-object tracking (MOT) is a fundamental task in computer vision, aiming to estimate objects (e.g., pedestrians and vehicles) bounding boxes and identities in video sequences. We propose a large-scale multi-object tracking dataset named SportsMOT, consisting of 240 video clips from 3 categories (i.e., basketball, football and volleyball). The objective is to only track players on the… See the full description on the dataset page: https://huggingface.co/datasets/MCG-NJU/SportsMOT.