fast-reid
PPYOLOE_pytorch
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| fast-reid | PPYOLOE_pytorch | |
|---|---|---|
| 3 | 1 | |
| 3,267 | 170 | |
| 1.4% | - | |
| 1.2 | 10.0 | |
| 4 months ago | almost 2 years ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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fast-reid
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DeepSort with PyTorch(support yolo series)
fast-reid
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Assign ID and track moving object with optical flow
On failure, you can try using a re-identification methods like FastReid: https://github.com/JDAI-CV/fast-reid in combination with your detector. A good pipeline that combines everything you seem to need is here: https://github.com/GeekAlexis/FastMOT. It uses a combination of Yolov4 (detector) + Kalman filters, Optical flow (tracker) and FastReid (re-identification)
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PPYOLOE_pytorch
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DeepSort with PyTorch(support yolo series)
Nioolek/PPYOLOE_pytorch
What are some alternatives?
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
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 ).
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
ScaledYOLOv4 - Scaled-YOLOv4: Scaling Cross Stage Partial Network
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
apex-configs-by-deafps - Apex config & tweaks
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Hekate-Toolbox - A toolbox for Hekate
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/