yolor
darknet
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yolor | darknet | |
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8 | 62 | |
1,971 | 21,418 | |
- | - | |
3.6 | 7.0 | |
4 months ago | 24 days ago | |
Python | C | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
yolor
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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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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
darknet
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Anybody building ML models in C++?
YoloV3/4 is C based if that counts: https://github.com/AlexeyAB/darknet
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[D] Fixing the angle of Skewed Paintings, see comments
This is all well-known information, see any (and all!) previous discussions when YOLOv5 comes up. For details: https://github.com/AlexeyAB/darknet/issues/5920
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Viseron 2.0.0 - Self-hosted, local only NVR and AI Computer Vision software.
Yes it is official, it is in another repo however, but it is linked from the main repo https://github.com/AlexeyAB/darknet
- Machine learning Library in C?
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GPL vs MIT.
Still to long. Here's my favourite license: https://github.com/AlexeyAB/darknet/blob/master/LICENSE
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Pretrained model on aerial object detection
(As an aside, it isn't just that YOLOv5 is evil, the problem is YOLOv5 is both slower and less precise than YOLOv4, and even then, no-one can reproduce the performance results they claim to get.) Source: https://github.com/AlexeyAB/darknet/issues/5920
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[D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
For a quick start on YOLOv3 / 4 follow the instructions here: https://github.com/AlexeyAB/darknet
- Does Multi Object Tracking work better (precision/recall) on videos than jury rigging a SOTA image object detection to work on videos?
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Which model is best for detecting small objects? Yolov3? MaskRCNN, Faster-RCNN?
YOLOv4-tiny-3L is what I use when I'm doing projects that require finding small objects. And for really tiny objects in large images where the object would be resized to zero (or near zero) due to network dimensions, then I use DarkHelp with tiling, combined with YOLOv4-tiny-3L. Depending on how precise the bounding boxes need to be, I also tend to use YOLOv4-tiny, which is faster but less precise than YOLO-v4-tiny-3L.
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
darknet_ros - YOLO ROS: Real-Time Object Detection for ROS
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
Alturos.Yolo - C# Yolo Darknet Wrapper (real-time object detection)
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
AlexNet - implement AlexNet with C / convolutional nerual network / machine learning / computer vision
ScaledYOLOv4 - Scaled-YOLOv4: Scaling Cross Stage Partial Network
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.