NRL Player Detection Object Detection Dataset and Pre-Trained Model by AAP BLOCKY

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Explore Dataset

This project involves annotating players on a rugby league field in a set of video frames. The goal is to label each player with a bounding box in each frame.

We have extracted around 1500 frames from rugby league videos, and we need to annotate the players in each frame. The labels should be accurate and consistent across all frames.

I've uploaded the dataset so you can use the built-in annotation tool to label each player with a bounding box. To get started, follow these steps:

Open the annotation tool and select the first frame in the dataset.
Use the rectangle tool to draw a bounding box around each player in the frame.
Add the label 'Player' to each bounding box
Move to the next frame in the dataset and repeat steps 3-4.
Continue annotating all frames in the dataset until all players are labeled.
We recommend exporting the labels in the YOLO format.

If you have any questions or concerns about the annotation process, please don't hesitate to reach out to us.

Thank you for your help with this project!

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            nrl-player-detection_dataset,
                            title = { NRL Player Detection Dataset },
                            type = { Open Source Dataset },
                            author = { AAP BLOCKY },
                            howpublished = { \url{ https://universe.roboflow.com/aap-blocky-yqzrb/nrl-player-detection } },
                            url = { https://universe.roboflow.com/aap-blocky-yqzrb/nrl-player-detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-10 },
                            }