Skip to content
BranchesTags

Latest commit

History

27 Commits

Folders and files

NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt

Repository files navigation

Aimify

An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.

Planned Features

  • Sleek and modern UI made to Material V3 guidelines

  • Automatic scoring via the use of instance segmentation models

  • Cross platform compatability through the use of React Native

  • User interaction and sharing

  • View all of your earned badges

Machine Learning

image

Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.

Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.

Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.

The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.

Current Progress

image

Figure 2: Current progress on arrowV1 model

The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.

User Interface (In the backlog)

image

Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.

The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components

Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.

Roadmap

  • Finish annotation of dataset (Completed 31/05/2024)

  • Train ML model by end of June 2024 (Ongoing)

  • Start UI by July 2024

  • Work on backend and server-side software by August 2024

  • Release alpha build by September 2024

Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.

A PDF-formatted version of this license can be found here.

About

An archery scoring app that integrates an instance segmentation model for automatic scoring.

aimify.fletchworks.dev

Resources

Readme
Activity

Stars

6 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - pixelglide/aimify: An archery scoring app that integrates an instance segmentation model for automatic scoring. · GitHub
Skip to content
BranchesTags
Latest commit
History
27 Commits
Folders and files
NameName
Last commit message
Last commit date
.expo
.expo
.vscode
.vscode
ML
ML
app/aimify
app/aimify
misc
misc
README.md
README.md
license.pdf
license.pdf
license.txt
license.txt
Repository files navigation
Aimify
An archery scoring app designed to end the use of paper score sheets by offering a modern and convenient alternative using Computer Vision.
Planned Features
  • Sleek and modern UI made to Material V3 guidelines
  • Automatic scoring via the use of instance segmentation models
  • Cross platform compatability through the use of React Native
  • User interaction and sharing
  • View all of your earned badges
Machine Learning
image
Figure 1: Images being labeled in CVAT, a free and open source dataset labeling tool.
Machine Learning will be used throughout Aimify to facilitate a key feature: automatic scoring.
Through a YoloV8 model trained on a dataset consisting of over 200 images of archery targets (currently in production), users can calculate their scores by simply holding their phone up to the target and pressing a single button.
The trained YoloV8 model will then be exported in the Tensorflow lite (.tflite) format, enabling on-device inferencing for an offline experience.
Current Progress
image
Figure 2: Current progress on arrowV1 model
The above image shows the first iteration of arrowV1. This version uses Roboflow's 3.0 instance segmentation model and is a work in progress. Using this model comes with some drawbacks, notably no easy way to download the model, and less than satisfactory result, causing approximatly 50% of the outer rings to not be masked. This can be fixed through using YoloV8 models and increasing the number of images in the dataset respectivly.
User Interface (In the backlog)
image
Figure 3: UI mockups created in Figma with the use of Material V3 components from Google.
The majority of archery scoring apps available today use dated UI designs, either from Material V2, or earlier. To combat this, Aimify will use Material V3 (Material you) theming and components
Aimify will be built using the React Native framework via the Expo implementation, allowing for a single codebase to be deployed to both Android and IOS. In addtion, Aimify will use React Native Paper, giving acsess to hundreds of Material V3 components to build the UI from.
Roadmap
  • Finish annotation of dataset (Completed 31/05/2024)
  • Train ML model by end of June 2024 (Ongoing)
  • Start UI by July 2024
  • Work on backend and server-side software by August 2024
  • Release alpha build by September 2024
Though this may be subject to change in the future, the current Aimify license is based on the GPLv3 license, with an added restriction preventing the software from being monetised.
A PDF-formatted version of this license can be found here.
About
An archery scoring app that integrates an instance segmentation model for automatic scoring. 
aimify.fletchworks.dev
Resources
Readme
Activity
Stars
6 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Used by
Contributors
Languages