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SKNet

Implemenation of Selective Kernel Networks by pytorch.

The architecture of SK is as follows

I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26, which did not reach the performance of 20.79 in the paper. If somebody know what caused the problem, please leave me a message.

The pretrained weights are provided below.

Requirement

  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow

Dataset

  • ImageNet-2012

Pretrained Model on ImageNet-2012

ArchitectureTop-1 errorPretrained model
SKNet50
(My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50
(paper)		20.79None

If you want to use my pretrained weight, you should do

  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)

The error curve of SKNet50 during my training process is shown below

error curve

About

Implemenation of Selective Kernel Networks by pytorch with pretrained weight

Topics

pretrained-modelresnetresnet-50sknetsknet-pytorch

Resources

Readme
Activity

Stars

13 stars

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0 watching

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2 forks
Report repository

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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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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
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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This repository was archived by the owner on Aug 27, 2022. It is now read-only.

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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
Stars
13 stars
Watchers
0 watching
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2 forks
Report repository
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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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This repository was archived by the owner on Aug 27, 2022. It is now read-only.

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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
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13 stars
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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" + '

Skip to content

This repository was archived by the owner on Aug 27, 2022. It is now read-only.

BranchesTags
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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
Stars
13 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
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, '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('^' + ".*" + '

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This repository was archived by the owner on Aug 27, 2022. It is now read-only.

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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
Stars
13 stars
Watchers
0 watching
Forks
2 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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This repository was archived by the owner on Aug 27, 2022. It is now read-only.

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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
Stars
13 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
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, '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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SKNet
Implemenation of Selective Kernel Networks by pytorch.
The architecture of SK is as follows
I trained SKNet50 on ImageNet-2012 from scratch and got an accuracy of 21.26,
which did not reach the performance of 20.79 in the paper.
If somebody know what caused the problem, please leave me a message.
The pretrained weights are provided below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • ImageNet-2012
Pretrained Model on ImageNet-2012
ArchitectureTop-1 errorPretrained model
SKNet50 
 (My Imp.)		21.26Google Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79None
If you want to use my pretrained weight, you should do
  1. place the downloaded pretrained model in runs/sknet_imagenet/86028 folder under this project
  2. config the attribute of runid and cuda in the config file configs/sknet_imagenet.yml
  3. run validata.py or test.py (For test, you should specify the img_path in the test.py)
The error curve of SKNet50 during my training process is shown below
error curve
About
Implemenation of Selective Kernel Networks by pytorch with pretrained weight
Topics
pretrained-modelresnetresnet-50sknetsknet-pytorch
Resources
Readme
Activity
Stars
13 stars
Watchers
0 watching
Forks
2 forks
Report repository
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