ScaledYOLOv4
D3Feat
| ScaledYOLOv4 | D3Feat | |
|---|---|---|
| 10 | 2 | |
| 2,017 | 250 | |
| - | - | |
| 0.0 | 0.0 | |
| 10 months ago | over 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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ScaledYOLOv4
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DeepSort with PyTorch(support yolo series)
WongKinYiu/ScaledYOLOv4
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[P] Oddly thresholded confidence scores on scaled yolov4 csp
I'm using a branch of the author's PyTorch repo
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Real time object detection and recognition
Take a look at yolov5 or scaled yolov4. They should both handle real-time training, at low enough resolution anyway; I don't know if there is any model that can do real-time detection on 4K videos. Don't pay attention to the version numbers, I think the scaled yolov4 is sliiiightly better performance.
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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
* Scaled-YOLOv4-code (Pytorch): https://github.com/WongKinYiu/ScaledYOLOv4
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Implementing Faster R-CNN in C
As for the BEST model, I would suggest you use Scaled YOLOv4 since it performs the best both on the cloud and edge devices.
- How do you add a class to coco classes in YOLO (CNN) object detection?
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How to catch up with trending computer vision open-source Github repos
You're out of sync, yolo-v5 is a controversial naming. And whatever side of the debate you are in state-of-the art is Scaled-Yolo v4: https://github.com/WongKinYiu/ScaledYOLOv4 https://arxiv.org/abs/2011.08036
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How to Train a Scaled-YOLOv4 Object Detection Model
To complicate matters further, the code for this improvement of YOLOv4 (published by one of the original YOLOv4 authors) is actually a fork of the YOLOv5 repo: https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp
D3Feat
- High accuracy AR localization based on 3D-to-3D model registration (demo on 1st gen HoloLens) - what do you think?
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How to Train a Scaled-YOLOv4 Object Detection Model
Yep: https://github.com/XuyangBai/D3Feat
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
unsupervisedRR - [CVPR 2021 - Oral] UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
Graph_Signal_Processing - Signal processing on graphs using torch_geometric.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
mmdetection - OpenMMLab Detection Toolbox and Benchmark
yolo_series_deepsort_pytorch - Deepsort with yolo series. This project support the existing yolo detection model algorithm (YOLOV8, YOLOV7, YOLOV6, YOLOV5, YOLOV4Scaled, YOLOV4, YOLOv3', PPYOLOE, YOLOR, YOLOX ).
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
PPYOLOE_pytorch - An unofficial implementation of Pytorch version PP-YOLOE,based on Megvii YOLOX training code.
deep_sort - Simple Online Realtime Tracking with a Deep Association Metric
fast-reid - SOTA Re-identification Methods and Toolbox
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/