11
good first issue
Good for newcomers
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yolor
Here are 15 public repositories matching this topic...
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
pytorch
yolo
implicit
representation
darknet
explicit
yolov4
scaled-yolov4
yolov4-csp
yolor
unified-network
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Jun 7, 2022 - Python
NVIDIA DeepStream SDK 6.1 / 6.0.1 / 6.0 configuration for YOLO models
deep-neural-networks
deep-learning
pytorch
nvidia
yolo
object-detection
deepstream
darknet
tensorrt
yolov3
yolov4
yolov5
ultralytics
nvidia-deepstream-sdk
yolor
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Updated
Jul 1, 2022 - C++
Support Yolov5(4.0)/Yolov5(5.0)/YoloR/YoloX/Yolov4/Yolov3/CenterNet/CenterFace/RetinaFace/Classify/Unet. use darknet/libtorch/pytorch/mxnet to onnx to tensorrt
mxnet
pytorch
unet
darknet
classify
libtorch
retinaface
centernet
centerface
yolov4
yolov5
batch-inference
yolor
onnx-tensorrt
yolox
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Updated
Aug 2, 2021 - C++
使用ONNXRuntime部署anchor-free系列的YOLOR，包含C++和Python两种版本的程序
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Updated
Sep 18, 2021 - C++
Demos for how to use the shared libs of Lite.AI.ToolKit🚀 🚀 🌟 . (https://github.com/DefTruth/lite.ai.toolkit)
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Updated
Sep 24, 2021 - C++
Experimental implementation of real-time object detection algorithm YOLOR on embedded systems (edge computing devices)
machine-learning
computer-vision
deep-learning
robotics
inference
artificial-intelligence
embedded-systems
yolo
object-detection
edge-computing
state-of-the-art
real-time-object-detection
nvidia-jetson
jetson-xavier-nx
nvidia-jetson-xavier-nx
yolor
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Updated
Feb 9, 2022 - Python
Final project of VRDL course in 2021 fall semester at NYCU.
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Updated
Mar 2, 2022 - Python
FSOD stands for Firearms and Sharp Object Detector. In conclusion, this dashboard is a web application made with streamlit that can detect several kind of firearms and sharp object threat. Object detection algorithm used to make the model are YOLO-R and also used Deepsort for tracking purpose.
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Updated
Jun 16, 2022 - Jupyter Notebook
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Apr 28, 2022 - Jupyter Notebook
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这个issue主要讲一下，如何把你自己的模型添加到lite.ai.toolkit。lite.ai.toolkit集成了一些比较新的基础模型，比如人脸检测、人脸识别、抠图、人脸属性分析、图像分类、人脸关键点识别、图像着色、目标检测等等，可以直接用到具体的场景中。但是，毕竟lite.ai.toolkit的模型还是有限的，具体的场景下，可能有你经过优化的模型，比如你自己训了一个目标检测器，可能效果更好。那么，如何把你的模型加入到lite.ai.toolkit中呢？这样既能用到lite.ai.toolkit一些已有的算法能力，也能兼容您的具体场景。这个issue主要是讲这个问题。大家有疑惑的可以提在这个issue，我会尽可能回答~