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DeepCompareJ

DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.

This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.

Requirements

To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.

Example

DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.

The interface of the Java application is the following:

Interface

Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:

  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.

The users also can select the measures used to compare the models. As in the previous case, the users have three options:

  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.

And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.

Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.

Results table

About

DeepCompareJ is a Java application to compare deep classification models

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n 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;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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DeepCompareJ
DeepCompareJ is a Java application that allows users to easily compare models from different deep learning frameworks without worrying about the particularities of each library. Users of this project can test different models and frameworks with several measures to solve their problems.
This Java appplication uses DeepClas4Bio API, an API that aims to facilitate the interoperability of bioimaging tools with deep learning frameworks, to compare different models.
Requirements
To use DeepCompareJ is necessary to have installed Java 8 and the DeepClas4Bio API.
Example
DeepCompareJ uses DeepClas4Bio API, so when DeepCompareJ starts, the application connects with DeepClas4Bio API to obtain all necessary information.
The interface of the Java application is the following:
Interface
Here the users can manage the models and frameworks they want to compare. For this task, the users have three options:
  • Add model: To add a pair framework-model to the list to compare.
  • Delete model: To remove one pair framework-model from the list to compare.
  • Delete all models: To clean the list to compare.
The users also can select the measures used to compare the models. As in the previous case, the users have three options:
  • Add measure: To add a measure to the list of measures to use.
  • Delete measure: To remove one measure from the list of measures to use.
  • Delete all measures: To clean the list of measures to use.
And finally, the users can manage the DataSet they want use. Here the users can select the way to load the DataSet (for example he API loads datasets from a folder where each class has its corresponding folder of images), the path of the DataSet and the text file where they have the different classes of the model.
Once the users have selected the models they want to compare, the measures they wnat to use and the DataSet they want to evaluate, DeepCompareJ connects with the DeepClas4Bio API to compare the selected models. The results of the comparison are presented in a table.
Results table
About
DeepCompareJ is a Java application to compare deep classification models
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages