isosurfaces
Construct isolines/isosurfaces of a 2D/3D scalar field defined by a function. (by jared-hughes)
yolor
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206) (by WongKinYiu)
Our great sponsors
| isosurfaces | yolor | |
|---|---|---|
| 2 | 8 | |
| 25 | 1,971 | |
| - | - | |
| 6.6 | 3.6 | |
| 2 months ago | 5 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
isosurfaces
Posts with mentions or reviews of isosurfaces.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-28.
-
Rendering algorithm used for implicit functions
For constructing the quadtree, I'd refer you to https://github.com/jared-hughes/isosurfaces (specifically build_tree and should_descend_deep_cell) which takes a similar approach as Desmos: descend to a certain depth, say 5 (to get a 32×32 grid), then descend only quads that cross the isoline (with a few more heuristics).
-
How does desmos render implicit plots?
Desmos uses the same approach as my isosurfaces Python package, based on J. Manson and S. Schaefer, "Isosurfaces Over Simplicial Partitions of Multiresolution Grids."
yolor
Posts with mentions or reviews of yolor.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-20.
-
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)
-
DeepSort with PyTorch(support yolo series)
WongKinYiu/yolor
-
Build Custom Functions for YOLOv4 with TensorFlow, TFLite & TensorRT
Is there a reason to use YOLOv4 over YOLOv5 or YOLOR?
-
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
-
[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
-
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
-
[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?
When comparing isosurfaces and yolor you can also consider the following projects:
NURBS-Python - Object-oriented pure Python B-Spline and NURBS library
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
graphest - A faithful graphing calculator
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Swar-Chia-Plot-Manager - This is a Cross-Platform Plot Manager for Chia Plotting that is simple, easy-to-use, and reliable.
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
dicom2stl - Python script to extract a STL surface from a DICOM image series.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
ScaledYOLOv4 - Scaled-YOLOv4: Scaling Cross Stage Partial Network
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
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.
ayolo - PyQt5 based annotation tool for yolov4 datasets, providing fast and easy ways of annotating.