diff --git a/lacrosse-object-detection/README.dataset.txt b/lacrosse-object-detection/README.dataset.txt new file mode 100644 index 0000000000000000000000000000000000000000..9a54b49ef0680f20521d4442ae4c3f9555ea87d7 --- /dev/null +++ b/lacrosse-object-detection/README.dataset.txt @@ -0,0 +1,73 @@ +# lacrosse-object-detection-uxkt > 2025-03-13 9:49am +https://universe.roboflow.com/rf-100-vl/lacrosse-object-detection-uxkt-vaybh + +Provided by a Roboflow user +License: MIT + +# Overview + +- [Introduction](#introduction) +- [Object Classes](#object-classes) + - [Goalie](#goalie) + - [Longpole](#longpole) + - [Referee](#referee) + - [Shortstick](#shortstick) + +# Introduction + +This dataset focuses on object detection in the sport of lacrosse. The goal is to accurately identify and annotate specific roles and equipment within the game. The classes include: + +- **Goalie**: The player responsible for preventing the ball from entering the goal. +- **Longpole**: A player or item with a longer stick used primarily by defenders. +- **Referee**: The official who oversees the game and enforces the rules. +- **Shortstick**: A player or item with a shorter stick used primarily by offensive players. + +# Object Classes + +## Goalie + +### Description + +The goalie is typically positioned near the goal and wears protective gear, including a helmet and pads. This class is characterized by the proximity to the goal and the specific protective stance typical of a goalie. The goalie's stick typically has a wide round net. + +### Instructions + +- Annotate the area encompassing the whole individual, including the head, body, and any distinguishable goalie gear. +- Ensure the bounding box includes the player when they are in a typical guarding stance or engaged in a save attempt. +- Exclude any part of the goal structure or other players if they intersect with the goalie's space. + +## Longpole + +### Description + +The longpole is identified by the player holding a visibly longer stick, which is used mainly in defensive roles. The stick length distinguishes this class from others. + +### Instructions + +- Annotate the whole player, centering on capturing the distinctively long stick. +- Include the full length of the pole to differentiate from a shortstick. +- Avoid labeling any players with short sticks or those without a visible long stick. + +## Referee + +### Description + +Referees wear distinct uniforms typically featuring a black and white striped or yellow shirt and are easily recognizable by their positioning and activity on the field. + +### Instructions + +- Annotate the entire person, ensuring the bounding box includes the typical uniform and observable refereeing gestures. +- Capture the referee when they are in clear view, making calls, or moving to oversee the play. +- Do not include players or any goalies, only the referee in uniform. + +## Shortstick + +### Description + +The shortstick pertains to players or equipment with a shorter stick used mainly by attackers and midfielders. These sticks are notably shorter than longpoles. + +### Instructions + +- Annotate the full player, ensuring the stick's length falls into the shorter category. +- Disambiguate from longpoles by focusing on the visibly shorter stick even when held at different angles. +- Exclude any players that appear with a long stick or those obstructed such that stick length is undetermined. \ No newline at end of file diff --git a/lacrosse-object-detection/README.roboflow.txt b/lacrosse-object-detection/README.roboflow.txt new file mode 100644 index 0000000000000000000000000000000000000000..cd07c3d09ec678f00b571c013d4c32f3aba053a5 --- /dev/null +++ b/lacrosse-object-detection/README.roboflow.txt @@ -0,0 +1,27 @@ + +lacrosse-object-detection-uxkt - v1 2025-03-13 9:49am +============================== + +This dataset was exported via roboflow.com on March 14, 2025 at 4:08 AM GMT + +Roboflow is an end-to-end computer vision platform that helps you +* collaborate with your team on computer vision projects +* collect & organize images +* understand and search unstructured image data +* annotate, and create datasets +* export, train, and deploy computer vision models +* use active learning to improve your dataset over time + +For state of the art Computer Vision training notebooks you can use with this dataset, +visit https://github.com/roboflow/notebooks + +To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com + +The dataset includes 500 images. +Lacrosse-object-detection-uxkt-uxkt are annotated in COCO format. + +The following pre-processing was applied to each image: + +No image augmentation techniques were applied. + + diff --git a/lacrosse-object-detection/test/_annotations.coco.json b/lacrosse-object-detection/test/_annotations.coco.json new file mode 100644 index 0000000000000000000000000000000000000000..1f347adf3b0744570b2a8afdf475e961ca34ddf2 --- /dev/null +++ b/lacrosse-object-detection/test/_annotations.coco.json @@ -0,0 +1 @@ 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