🔥 Massive Scale: BASKET features 4,477 hours of video showcasing 32,232 basketball players from across the globe!

🔥 Extensive Diversity: Spanning 21 basketball leagues, both professional and amateur, featuring over 7,000 female players and detailed skill level annotations across 20 abilities!

🔥 Versatile Applications: BASKET supports advanced video model development and enables domain-specific applications like fair scouting and personalized player development.

Introduction

We present BASKET, a large-scale basketball video dataset for fine-grained skill estimation. BASKET contains 4,477 hours of video capturing 32,232 basketball players from all over the world. Compared to prior skill estimation datasets, our dataset includes a massive number of skilled participants with unprecedented diversity in terms of gender, age, skill level, geographical location, etc. BASKET includes 20 fine-grained basketball skills, challenging modern video recognition models to capture the intricate nuances of player skill through in-depth video analysis. Given a long highlight video (8-10 minutes) of a particular player, the model needs to predict the skill level (e.g., excellent, good, average, fair, poor) for each of the 20 basketball skills. Our empirical analysis reveals that the current state-of-the-art video models struggle with this task, significantly lagging behind the human baseline. We believe that BASKET could be a useful resource for developing new video models with advanced long-range, fine-grained recognition capabilities. In addition, we hope that our dataset will be useful for domain-specific applications such as fair basketball scouting, personalized player development, and many others.

Requirements

Please follow the installation instructions for each model repository.

Dataset & Annotations

BASKET Download

To download the BASKET dataset, please agree with the terms on Hugging Face.

Data Structure

$DATASET_ROOT
├── BASKET
|  ├── 18-19
|     ├── NBA
|        ├── 1_18_19.mp4
|        ...
|        └── 96713_18_19.mp4
|     ...
|     └── NCAA Division I
|        ├── 113761_18_19.mp4
|        ...
|        └── 560857_18_19.mp4
|        ...
|  ...
|  └── 22-23
|     ├── FIBA Europe Cup
|     ...
|     └── China. CBA

$ANNOTATIONS_ROOT
|  ├── BASKET_Labels
|     ├── BASKET
|        ├── train.csv
|           └── Player 1, skill 1 level ... skill 20 level
|        ├── val.csv
|        └── test.csv
|     ├── cross_season
|        └── test.csv
|     ├── cross_league
|        └── test.csv
|     ├── cross_season_and_league
|        └── test.csv

Annotation Structure

The CSV file includes the video path for each player along with their corresponding ratings for 20 skills. Conceptually, a rating of 0 indicates poor performance in a skill, while a rating of 4 represents excellence.

We recommand changing the annotation file video path to complete path once downloaded. e.g. /User/my_user_name/BASKET_video/18-19/...mp4
Conceptually for the defensive consistency skill, a lower rating is better (i.e., 0 represents excellent defense).

Checkpoint

You can find each model's checkpoint here

Training

We trained and tested on 8 NVIDIA RTX A6000 GPUs. Results may slightly vary due to non-fixed random seeds, GPU specifications, and data transmission.

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