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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]

@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}

Overview

Reduced training cost and improved performance

  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!

Superior performance across the pareto frontier

Role of different contributions to overall performance and cost

How to use

Evaluate checkpoints

To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)

Train your supernetwork

MobileNetV3

horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless

train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements

  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2

Check setup/ for environment setup instructions

About

Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

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" + '

Skip to content
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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
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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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
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
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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
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
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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 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('^' + ".*" + '

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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
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Readme
Apache-2.0 license
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2 stars
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1 watching
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, '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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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 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); }
})();
})();

Skip to content
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DϵpS: Delayed ϵ-Shrinking for Faster Once-For-All Training [arxiv]
@inproceedings{deps-eccv2024,
title={D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training},
author={Aditya Annavajjala^{*} and Alind Khare^{*} and Animesh Agrawal and Igor Fedorov and Hugo Latapie and Myungjin Lee and Alexey Tumanov},
booktitle = {Proc. of the 18th European Conference on Computer Vision},
series = {ECCV '24},
month = {August},
year = {2024},
url={https://arxiv.org/abs/2407.06167},
}
Overview
Reduced training cost and improved performance
  • DϵpS significantly reduces training cost while improving performance of subnetworks in the lower FLOPs range!
Superior performance across the pareto frontier
Role of different contributions to overall performance and cost
How to use
Evaluate checkpoints
To evaluate a checkpoint run the script located at deps/evaluate.py (it has the necessary instructions)
Train your supernetwork
MobileNetV3
horovodrun -np 16 --start-timeout 300 -H 130.207.125.17:8,130.207.125.19:8 --network-interface=ens8f0 \
python train_net.py --task maxnet --exp_id deps@eccv_test --n_epochs 270 \
--base_lr 0.1625 --opt_type sgd --lr_schedule_type cosine --lr_schedule_param 2 \
--ks_list 7 --depth_list "2, 3, 4" --expand_list "3, 4, 6" --dropout 0.1 \
--dynamic_batch_size 4 --weight_decay 3e-5 --momentum 0.9 --base_batch_size 128 \
--bn_momentum 0.99 --lr_gamma 0.973 --dataset imagenet --label_smoothing 0.1 \
--auto_augment "imagenet" --n_worker 16 --bignas_lr_decay_step_size 0 --teacher_warmup 150 \
--gradient_aggregation sum --inplace_distillation --reorganize_weights --smallnet_warmup 5 \
--network_family mbv3 --distort_color torch --random_erase_prob 0.2 --mixup_alpha 0 --cutmix_alpha 0 --wandb

Proxyless
train_net.py --task teacher --exp_id RES5_Proxyless_Largest_16GPU \
--n_epochs 300 --base_lr 0.0125 --opt_type sgd --lr_schedule_type cosine \
--ks_list 7 --depth_list 4 --expand_list 6 --dropout 0 --dynamic_batch_size 1 \
--weight_decay 5e-5 --momentum 0.9 --base_batch_size 64 --bn_momentum 0.99 \
--dataset imagenet --label_smoothing 0.1 --n_worker 8 --lr_gamma 1 --warmup_epochs 5 \
--manual_seed 42 --random_erase_prob 0.2 --mixup_alpha 0.1 --cutmix_alpha 0.1 \
--warmup_lr 0 --wandb --network_family proxyless --distort_color torch

Requirements
  • Python 3.6.13+
  • Pytorch 1.10.0+
  • Horovod 0.19.2
Check setup/ for environment setup instructions
About
Official release of DepS: Delayed Eps-Shrinking for Faster Once-For-All Training, ECCV 2024
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages