yolo-tf2
yolor
Our great sponsors
| yolo-tf2 | yolor | |
|---|---|---|
| 1 | 8 | |
| 747 | 1,971 | |
| - | - | |
| 7.6 | 3.6 | |
| almost 2 years ago | 4 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yolo-tf2
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How to write a resume for python / ML jobs?
my most useful project is yolo object detector implementation in tf2 and I'm currently working on 2 other projects, one of which is the implementation of various drl algorithms in tf and the other project will be based on the latter and it's concerned with trading. The rest are more of scripts rather than projects ex: web scraping, file management, programming challenges ...
yolor
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Explicit and Implicit Knowledge in Object Detection (YOLOR, YOLOv7)
Fellow redditors, can you please explain to me how aforementioned structures work and applied in code? I tried to read carefully the papers on YOLOv7 and YOLOR (https://arxiv.org/pdf/2207.02696.pdf, https://arxiv.org/pdf/2105.04206.pdf) but for me it feels like explanations in text have literally no relation to implementation code (I am totally not into Torch so it makes understanding even harder) (https://github.com/WongKinYiu/yolor/blob/main/utils/layers.py)
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DeepSort with PyTorch(support yolo series)
WongKinYiu/yolor
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Build Custom Functions for YOLOv4 with TensorFlow, TFLite & TensorRT
Is there a reason to use YOLOv4 over YOLOv5 or YOLOR?
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Docker for Absolute Beginners.
I am interested in using Docker for Deep learning models use. On Github people recommend Docker environment to use the model. I am sharing the link to the Github repo. My question is how I can use this GitHub repo and create a docker container
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[Project]Vehicle Counting + Speed Calculation using YOLOR+ DeepSORT OpenCV Python
So there is a paper on YOLOR by Wong Kin Yiu https://github.com/WongKinYiu/yolor
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YOLOR (Scaled-YOLOv4-based): The best speed/accuracy ratio for Waymo autonomous driving challenge
[CVPR'21 WAD] Challenge - Waymo Open Dataset: https://waymo.com/open/challenges/2021/real-time-2d-prediction/ YOLOR (Scaled-YOLOv4-based) has the best speed/accuracy ratio on Waymo autonomous driving challenge ((Waymo Open Dataset): Real-time 2D Detection. Thanks Chien-Yao Wang from Academia Sinica and DiDi MapVision team to push Scaled-YOLOv4 further! * DIDI MapVision: https://arxiv.org/abs/2106.08713 * YOLOR https://arxiv.org/abs/2105.04206 * YOLOR-code (Pytorch): https://github.com/WongKinYiu/yolor * Scaled-YOLOv4(CVPR21): https://openaccess.thecvf.com/content/CVPR2021/html/Wang\_Scaled-YOLOv4\_Scaling\_Cross\_Stage\_Partial\_Network\_CVPR\_2021\_paper.html * Scaled-YOLOv4-code (Pytorch): https://github.com/WongKinYiu/ScaledYOLOv4 * YOLOv4: https://arxiv.org/abs/2004.10934 * YOLOv4-code (Darknet, Pytorch, TensorFlow, TRT, OpenCV…): https://github.com/AlexeyAB/darknet#yolo-v4-in-other-frameworks
The DiDi MapVision team has shown excellent results with the YOLOR and DIDI MapVision models, both based on Scaled-YOLOv4: * DIDI MapVision: https://arxiv.org/abs/2106.08713 * YOLOR https://arxiv.org/abs/2105.04206 * YOLOR-code (Pytorch): https://github.com/WongKinYiu/yolor * Scaled-YOLOv4(CVPR21): https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html * Scaled-YOLOv4-code (Pytorch): https://github.com/WongKinYiu/ScaledYOLOv4 * YOLOv4: https://arxiv.org/abs/2004.10934 * YOLOv4-code (Darknet, Pytorch, TensorFlow, TRT, OpenCV...): https://github.com/AlexeyAB/darknet#yolo-v4-in-other-frameworks
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[P] YOLOR (Scaled-YOLOv4-based): The best speed/accuracy ratio for Waymo autonomous driving challenge
* YOLOR-code (Pytorch): https://github.com/WongKinYiu/yolor
What are some alternatives?
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3 - This is a python program using YOLO and OpenCV to detect traffic lights. Works in The Netherlands, possibly other countries
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
ScaledYOLOv4 - Scaled-YOLOv4: Scaling Cross Stage Partial Network
onnx-tensorflow - Tensorflow Backend for ONNX
MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.