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CNNs for image classification

Train CNNs for image classification from scratch.

I post several pretrained weights below.

Requirement

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

Dataset

  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012

Usage

  • Add configuration file under configs folder as follows 
    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl
  • run train.py, validate.py or test.py as follows 
    • python train.py --config configs/aaaa.yml

Pretrained Model on ImageNet 2012

ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18
(My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18
(paper)		30.43---
ResNet50
(My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50
(paper)		24.7---
ResNet101
(My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101
(paper)		22.44---
ResNeXt50
(My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50
(paper)		22.2---
SE ResNet50
(My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50
(paper)		23.29---
CBAM ResNet50
(My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50
(paper)		22.66---
SKNet50
(My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50
(paper)		20.79---
MobileNet V2 0.5x
(My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x
(paper)		35.6---
MobileNet V2 1x
(My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x
(paper)		28.0---
MobileNet V3 large
(My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large
(paper)		24.8---

The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.

And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.

Pretrained weights usage

  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project, where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.

About

pytorch implementation of several CNNs for image classification

Topics

cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch

Resources

Readme
Activity

Stars

17 stars

Watchers

0 watching

Forks

1 fork
Report repository

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Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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This repository was archived by the owner on Aug 27, 2022. It is now read-only.

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CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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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CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content

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

BranchesTags
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CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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
Repository files navigation
CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
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('^' + ".*" + '

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CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
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.

BranchesTags
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CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
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); }
})();
})();

Skip to content

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

BranchesTags
Repository files navigation
CNNs for image classification
Train CNNs for image classification from scratch.
I post several pretrained weights below.
Requirement
  • pytorch 1.4.0+
  • torchvision
  • tensorboard 1.14+
  • numpy
  • pyyaml
  • tqdm
  • pillow
Dataset
  • CIFAR-10
  • CIFAR-100
  • ImageNet 2012
Usage
  • Add configuration file under configs folder as follows

    • cuda: "all" # if not specified, use cpu. or specified as "0", "0,1"
model:
arch: resnet
depth: 50
data:
dataset: imagenet
train_dir: /PATH/TO/ILSVRC/train
val_dir: /PATH/TO/ILSVRC/val
workers: 16
training:
runid: xxxx # recommended specified during validation and testing
epochs: 100
batch_size: 256
loss:
name: 'label_smooth'
smoothing: 0.1
optimizer:
name: 'sgd'
lr: 0.1
weight_decay: 0.0001
momentum: 0.9
lr_schedule:
name: 'multi_step'
milestones: [30,60,90]
gamma: 0.1
save_interval: 1
resume: save_model.pkl
best_model: best_model.pkl

  • run train.py, validate.py or test.py as follows

    • python train.py --config configs/aaaa.yml
Pretrained Model on ImageNet 2012
ArchitectureTop-1 errorParamsFLOPsPretrained weights
ResNet18 
 (My Imp.)		29.7211.69M1.82GGoogle Drive
Baidu Netdisk
ResNet18 
 (paper)		30.43---
ResNet50 
 (My Imp.)		23.3025.56M4.11GGoogle Drive
Baidu Netdisk
ResNet50 
 (paper)		24.7---
ResNet101 
 (My Imp.)		22.1844.55M7.84GGoogle Drive
ResNet101 
 (paper)		22.44---
ResNeXt50 
 (My Imp.)		22.3525.03M4.26GGoogle Drive
Baidu Netdisk
ResNeXt50 
 (paper)		22.2---
SE ResNet50 
 (My Imp.)		22.6428.09M4.12GGoogle Drive
Baidu Netdisk
SE ResNet50 
 (paper)		23.29---
CBAM ResNet50 
 (My Imp.)		22.4028.07M4.13GGoogle Drive
CBAM ResNet50 
 (paper)		22.66---
SKNet50 
 (My Imp.)		21.2627.49M4.50GGoogle Drive
Baidu Netdisk
SKNet50 
 (paper)		20.79---
MobileNet V2 0.5x 
 (My Imp.)		35.621.97M138.46MGoogle Drive
MobileNet V2 0.5x 
 (paper)		35.6---
MobileNet V2 1x 
 (My Imp.)		28.093.50M315.41MGoogle Drive
Baidu Netdisk
MobileNet V2 1x 
 (paper)		28.0---
MobileNet V3 large 
 (My Imp.)		26.795.48M230.05MGoogle Drive
MobileNet V3 large 
 (paper)		24.8---
The hyperparameters and settings during my training for ResNet, ResNeXt, SENet, SKNet are the same as the paper, except I use label smooth loss.
And for MobileNet V2 and MobileNet V3, I follow the setup in this project, and use label smooth loss too.
Pretrained weights usage
  1. place the downloaded pretrained model in runs/aaaa/xxxx folder under this project,
where aaaa is the name of configuration file and xxxx is runid in configuration file.
  2. run validate.py or test.py as above.
About
pytorch implementation of several CNNs for image classification
Topics
cbamcifarcnnimage-classificationimagenetpretrained-weightspytorchresnetresnextsenetsknettrain-from-scratch
Resources
Readme
Activity
Stars
17 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages