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Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch

License: GPL v3

About • Installation • Examples • Inference • Contribution • License

Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.

What is NNcore

NNcore is a deep learning framework focusing on solving autonomous-driving problems.

  • Task-based training and export for multiple framework

Installation

Pip / conda

pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations

To install nncore and develop locally

git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .

Examples

We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder

  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset

Perform inference

Export PyTorch checkpoint to ONNX

Use the script provided in examples, then run the below command

python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output

Performing inference

See serve library

Contribution

If you want to contribute to nncore, please follow steps below:

  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request

To run tests

pip install pytest
python -m pytest test/

To run code-format

pip install pre-commit
pre-commit install
pre-commit run -a

License

See LICENSE

About

nncore is a pytorch framework focusing on solving autonomous-driving problems.

Topics

autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt

Resources

Readme
GPL-3.0 license
Activity
Custom properties

Stars

19 stars

Watchers

2 watching

Forks

5 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
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");
}
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
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
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
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
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
19 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Collection of tasks for fast baseline solving in self-driving problems with deep learning. The offical language support is Pytorch
License: GPL v3
About •
Installation •
Examples •
Inference •
Contribution •
License
Note: This pkg is currently in development. Please open an issue if you find anything that isn't working as expected.
What is NNcore
NNcore is a deep learning framework focusing on solving autonomous-driving problems.
  • Task-based training and export for multiple framework
Installation
Pip / conda
pip install --upgrade --force-reinstall --no-deps albumentations
pip install qudida
pip install git+https://github.com/HCMUS-ROBOTICS/ssdf-nncore
Other installations
To install nncore and develop locally
git clone https://github.com/HCMUS-ROBOTICS/ssdf-nncore nncore
cd nncore
pip install -e .
Examples
We provide some examples here. You can use the interactive version by the colab notebooks belows. For more detail, you can refer to this folder
  • Segmentation on LyftDataset Segmentation on LyftDataset
  • Segmentation on UITDataset Segmentation on UITDataset
Perform inference
Export PyTorch checkpoint to ONNX
Use the script provided in examples, then run the below command
python3 torch2onnx.py <checkpoint> --in_shape 1 3 224 224 --inputs input --outputs output
Performing inference
See serve library
Contribution
If you want to contribute to nncore, please follow steps below:
  1. Fork your own version from this repository
  2. Checkout to another branch, e.g. fix-loss, add-feat.
  3. Make changes/Add features/Fix bugs
  4. Add test cases in the test folder and run them to make sure they are all passed (see below)
  5. Run code format to check formating before making a commit (see below)
  6. Push the commit(s) to your own repository
  7. Create a pull request
To run tests
pip install pytest
python -m pytest test/
To run code-format
pip install pre-commit
pre-commit install
pre-commit run -a
License
See LICENSE
About
nncore is a pytorch framework focusing on solving autonomous-driving problems.
Topics
autodriveautonomous-vehiclesdeep-learninglyftpytorchsegmentationself-driving-carsemantic-segmentationtensorrt
Resources
Readme
GPL-3.0 license
Activity
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19 stars
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2 watching
Forks
5 forks
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