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EOD

image

Easy and Efficient Object Detector

EOD (Easy and Efficient Object Detection) is a general object detection model production framework. It aim on provide two key feature about Object Detection:

  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.

The master branch works with PyTorch 1.8.1. Due to the pytorch version, it can not well support the 30 series graphics card hardware.

Install

pip install -r requirments

Get Started

Some example scripts are supported in scripts/.

Export Module

Export eod into ROOT and PYTHONPATH

ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH

Train

Step1: edit meta_file and image_dir of image_reader:

dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR

Step2: train

python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch

Step3: fp16, add fp16 setting into runtime config

runtime:
fp16: True

Eval

Step1: edit config of evaluating dataset

Step2: test

python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test

Demo

Step1: add visualizer config in yaml

inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5

Step2: inference

python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results

Mpirun mode

EOD supports mpirun mode to launch task, MPI needs to be installed firstly

# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install

Launch task

mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi

Custom Example

Benckmark

Quick Run

Tutorials

Useful Tools

References

Acknowledgments

Thanks to all past contributors, especially opcoder,

About

Easy and Efficient Object Detector

Resources

Readme
Apache-2.0 license
Activity

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 
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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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EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
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
EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
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
EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
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
EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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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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EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
EOD
image
Easy and Efficient Object Detector
EOD (Easy and Efficient Object Detection) is a general object detection model production framework.
It aim on provide two key feature about Object Detection:
  • Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compression (quantization/sparsity) will be well addressed.
  • Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
  • Large-Scale Dataset Training Detail
  • Equalized Focal Loss for Dense Long-Tailed Object Detection EFL
  • Improve-YOLOX YOLOX-RET
  • Quantization Aware Training(QAT) interface based on MQBench.
The master branch works with PyTorch 1.8.1.
Due to the pytorch version, it can not well support the 30 series graphics card hardware.
Install
pip install -r requirments
Get Started
Some example scripts are supported in scripts/.
Export Module
Export eod into ROOT and PYTHONPATH
ROOT=../../
export ROOT=$ROOTexport PYTHONPATH=$ROOT:$PYTHONPATH
Train
Step1: edit meta_file and image_dir of image_reader:
dataset:
type: coco # dataset typekwargs:
source: trainmeta_file: coco/annotations/instances_train2017.json image_reader:
type: fs_opencvkwargs:
image_dir: coco/train2017color_mode: BGR
Step2: train
python -m eod train --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1| tee log.train
  • --config: yamls in configs/
  • --nm: machine number
  • --ng: gpu number for each machine
  • --launch: slurm or pytorch
Step3: fp16, add fp16 setting into runtime config
runtime:
fp16: True
Eval
Step1: edit config of evaluating dataset
Step2: test
python -m eod train -e --config configs/det/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1| tee log.test
Demo
Step1: add visualizer config in yaml
inference:
visualizer:
type: pltkwargs:
class_names: ['__background__', 'person'] # class namesthresh: 0.5
Step2: inference
python -m eod inference --config configs/det/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir
  • --ckpt: model for inferencing
  • -i: images directory or single image
  • -v: directory saving visualization results
Mpirun mode
EOD supports mpirun mode to launch task, MPI needs to be installed firstly
# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/
tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure --prefix=/usr/local/mpich-3.2.1
make && make install
Launch task
mpirun -np 8 python -m eod train --config configs/det/yolox/yolox_tiny.yaml --launch mpi 2>&1| tee log.train
  • Add mpirun -np x; x indicates number of processes
  • Mpirun is convenient to debug with pdb
  • --launch: mpi
Custom Example
Benckmark
Quick Run
Tutorials
Useful Tools
References
Acknowledgments
Thanks to all past contributors, especially opcoder,
About
Easy and Efficient Object Detector
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages