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Start training models (including ViTs) with NetsPresso Trainer, compress and deploy your model with NetsPresso!

Table of contents

Installation (Stable)

Prerequisites

  • Python >=3.10
  • PyTorch >=2.0.1

Install with pypi

pip install netspresso_trainer

Install with GitHub

pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master

To install with editable mode,

git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer

Set-up with docker

Please clone this repository and refer to Dockerfile and docker-compose-example.yml.
For docker users, we provide more detailed guide in our Docs.

Getting started

Write your training script in train.py like:

fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")

Then, train your model with your own configuraiton:

python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml

Or you can start NetsPresso Trainer by just executing console script which has same feature.

netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml

Please refer to scripts/example_train.sh.

NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.

Dataset preparation (Local)

NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.

If you are interested in utilizing open datasets, you can use them by following the instructions.

Image classification

Semantic segmentation

Object detection

Pose estimation

Dataset preparation (Huggingface)

NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.

Pretrained weights

Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.

Tensorboard

We provide basic tensorboard to track your training status. Run the tensorboard with the following command:

tensorboard --logdir ./outputs --port 50001 --bind_all

where PORT for tensorboard is 50001.
Note that the default directory of saving result will be ./outputs directory.

About

A library for training, compressing and deploying computer vision models (including ViT) with edge devices

nota-netspresso.github.io/netspresso-trainer/

Resources

Readme
License

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, '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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Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation


Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 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
BranchesTags
Repository files navigation


Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 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
BranchesTags
Repository files navigation


Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
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('^' + ".*" + '

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Repository files navigation


Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
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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Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
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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Start training models (including ViTs) with NetsPresso Trainer,
compress and deploy your model with NetsPresso!

Table of contents
Installation (Stable)
Prerequisites
  • Python >=3.10
  • PyTorch >=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.

For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
fromnetspresso_trainerimporttrain_cliif__name__=='__main__':
logging_dir=train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.

Note that the default directory of saving result will be ./outputs directory.
About
A library for training, compressing and deploying computer vision models (including ViT) with edge devices
nota-netspresso.github.io/netspresso-trainer/
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages