--- title: "HockeyRink" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c3332a0d-59a0-4b6e-bc7e-1230aa4b5045" updated: "2026-11-12" --- # HockeyRink > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds 56 landmark points on an ice hockey rink in a picture and draws them with IDs and confidence scores. Built for sports analysts, broadcast graphics teams and vision researchers. HockeyRink is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by SimulaMet-HOST ## About ### What it is This Space finds 56 landmark points on an ice hockey rink in a picture and draws them on the image, each with an ID and a confidence score. It runs a YOLOv8 pose model trained on a rink dataset. That saves you from marking rink lines by hand before any spatial analysis. ### Who it suits It suits sports analysts, broadcast graphics teams and computer vision researchers who need to know where the rink sits in a camera frame. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Sports broadcasting | Graphics teams | Anchor overlays to the rink surface | Placing a graphic on the ice from a wide camera shot | | Sports analytics | Analysts | Map players onto a rink plan | Turning a frame into rink coordinates for game statistics | | Computer vision | Researchers | Test keypoint detection on sports footage | Checking how it copes when players block landmarks | | AR and VR | Developers | Line up virtual scenes with a real rink | Fitting a virtual scene to a rink view | People who work with other sports, or with no rink images at all, can skip it. ### Where to use it - A web browser: open the Space page on Hugging Face and upload an image - Hugging Face Spaces: it runs there on a basic CPU machine - Windows, macOS or Linux: run your own copy with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Camera calibration | Vision engineers | Rink landmark points to feed homography estimation | | Broadcast overlays | Graphics teams | Keypoint positions that show where the rink lies in the frame | | Player tracking | Analysts | Fixed rink reference points to place players against | ### How to set it up Using the online Space needs nothing installed. To run a copy on your own computer you need these first: - Python on Windows, macOS or Linux - The packages in requirements.txt, with Gradio 5.12.0 1. Install the Hugging Face hub package. 2. Download the Space into a folder named HockeyRink. 3. Move into that folder. 4. Install Gradio 5.12.0, then the other packages. 5. Start the app. ```sh pip install -U huggingface_hub hf download SimulaMet-HOST/HockeyRink --repo-type space --local-dir HockeyRink cd HockeyRink pip install gradio==5.12.0 pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space page and upload a picture of a hockey rink. On your own computer, start the app with this command and use it the same way. ```sh python app.py ``` The model detects the keypoints and shows each one on the picture with its ID and confidence score. You can feed those results into later spatial work or plots. ### Example You upload a wide-angle frame of a game where players partly block the rink. The Space returns the same picture with the detected keypoints marked, each labeled with its ID and confidence score. ### For newcomers and experts If you are new to this, think of it as a tool that finds fixed rink landmarks in a photo. Open the Space page and try one of its example pictures, or upload your own rink image. If you work in the field, the model is a YOLOv8-Large pose estimator that takes RGB images at any resolution and returns 56 keypoint coordinates, each paired with a confidence score. The card reports average mAP at 0.5 of 97.48%, mAP from 0.5 to 0.95 of 76.45%, precision of 96.21% and recall of 96.24%. It was trained on Swedish Hockey League footage, and the card gives no results for rinks from other leagues. Upload a rink image → The model finds 56 keypoints → Each point gets an ID and a score → The app draws them on the image ### Good to know The card lists speeds measured on two machines. Speed in frames per second | Label | Value | | --- | --- | | Tesla T4 GPU | 13.64 FPS | | M3 MacBook Pro | 6.4 FPS | - License: MIT - Cost to run: none named - Data it reaches: the images you upload to the Space or to your own copy ## README ````markdown # HockeyRink Finds 56 landmark points on an ice hockey rink in a picture and draws them with IDs and confidence scores. Built for sports analysts, broadcast graphics teams and vision researchers. Made by SimulaMet-HOST on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space finds 56 landmark points on an ice hockey rink in a picture and draws them on the image, each with an ID and a confidence score. It runs a YOLOv8 pose model trained on a rink dataset. That saves you from marking rink lines by hand before any spatial analysis. Who it suits: It suits sports analysts, broadcast graphics teams and computer vision researchers who need to know where the rink sits in a camera frame. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Sports broadcasting | Graphics teams | Anchor overlays to the rink surface | Placing a graphic on the ice from a wide camera shot Sports analytics | Analysts | Map players onto a rink plan | Turning a frame into rink coordinates for game statistics Computer vision | Researchers | Test keypoint detection on sports footage | Checking how it copes when players block landmarks AR and VR | Developers | Line up virtual scenes with a real rink | Fitting a virtual scene to a rink view ``` People who work with other sports, or with no rink images at all, can skip it. Where to use it: - A web browser: open the Space page on Hugging Face and upload an image - Hugging Face Spaces: it runs there on a basic CPU machine - Windows, macOS or Linux: run your own copy with Python ```table Title: Jobs it does Use case | Who | What they get back Camera calibration | Vision engineers | Rink landmark points to feed homography estimation Broadcast overlays | Graphics teams | Keypoint positions that show where the rink lies in the frame Player tracking | Analysts | Fixed rink reference points to place players against ``` How to set it up: Using the online Space needs nothing installed. To run a copy on your own computer you need these first: - Python on Windows, macOS or Linux - The packages in requirements.txt, with Gradio 5.12.0 1. Install the Hugging Face hub package. 2. Download the Space into a folder named HockeyRink. 3. Move into that folder. 4. Install Gradio 5.12.0, then the other packages. 5. Start the app. ```sh pip install -U huggingface_hub hf download SimulaMet-HOST/HockeyRink --repo-type space --local-dir HockeyRink cd HockeyRink pip install gradio==5.12.0 pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space page and upload a picture of a hockey rink. On your own computer, start the app with this command and use it the same way. ```sh python app.py ``` The model detects the keypoints and shows each one on the picture with its ID and confidence score. You can feed those results into later spatial work or plots. Example: You upload a wide-angle frame of a game where players partly block the rink. The Space returns the same picture with the detected keypoints marked, each labeled with its ID and confidence score. For newcomers and experts: If you are new to this, think of it as a tool that finds fixed rink landmarks in a photo. Open the Space page and try one of its example pictures, or upload your own rink image. If you work in the field, the model is a YOLOv8-Large pose estimator that takes RGB images at any resolution and returns 56 keypoint coordinates, each paired with a confidence score. The card reports average mAP at 0.5 of 97.48%, mAP from 0.5 to 0.95 of 76.45%, precision of 96.21% and recall of 96.24%. It was trained on Swedish Hockey League footage, and the card gives no results for rinks from other leagues. ```flow Upload a rink image The model finds 56 keypoints Each point gets an ID and a score The app draws them on the image ``` Good to know: The card lists speeds measured on two machines. ```chart Title: Speed in frames per second Tesla T4 GPU: 13.64 FPS M3 MacBook Pro: 6.4 FPS ``` - License: MIT - Cost to run: none named - Data it reaches: the images you upload to the Space or to your own copy ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink - Files: https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [SimulaMet-HOST/HockeyRink on Hugging Face](https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | HockeyRink (this page) | 1 | gradio | mit | 2025-01-16 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-01-16 - First published: 2025-01-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open SimulaMet-HOST/HockeyRink on Hugging Face](https://huggingface.co/spaces/SimulaMet-HOST/HockeyRink) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c3332a0d-59a0-4b6e-bc7e-1230aa4b5045) - [Its page on Mindket](https://mindket.com/p/c3332a0d-59a0-4b6e-bc7e-1230aa4b5045)