fast-reid
YOLOv6
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| fast-reid | YOLOv6 | |
|---|---|---|
| 3 | 11 | |
| 3,273 | 5,530 | |
| 1.6% | 1.3% | |
| 1.2 | 6.7 | |
| 5 months ago | about 2 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | 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.
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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)
- Safari translation won't work when lang="en" attribute is set!
YOLOv6
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I want to make a Class monitoring system. is it possible in the conditions I'm in ??
Some resources to get you started...https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606https://github.com/OlafenwaMoses/ImageAIhttps://towardsdatascience.com/yolo-object-detection-with-opencv-and-python-21e50ac599e9https://github.com/meituan/YOLOv6
- [P] Any object detection library
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DeepSort with PyTorch(support yolo series)
meituan/YOLOv6
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Tried to install requirements.txt with pip for YOLOv6.
Have you looked at this open github issue? It might be that you do not need to/should not install it using pip.
- A single-stage object detection framework dedicated to industrial applications
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YOLOv6: Redefine state-of-the-art for object detection
https://github.com/meituan/YOLOv6/blob/main/docs/About_namin...
> P.S. We are contacting the authors of YOLO series about the naming of YOLOv6.
You should ask _before_ publishing, not _after_.
They claim it runs faster and is more accurate than YOLOv5, yet requires 3x as much computation (GFLOPs)? Something doesn't add up here.
There is unbelievably little information about the architecture too. Unfortunately it's not in a format I can easily throw the cfg in as visualize it: https://gitlab.com/danbarry16/darknet-visual
This appears to be on purpose to advertise DagsHub: https://dagshub.com/pricing
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[D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
Saved you the time: https://github.com/meituan/YOLOv6
- Is YOLOv6 actually a significant improvement over YOLOv5?
- YOLOv6 is out
What are some alternatives?
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
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.
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
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/
apex-configs-by-deafps - Apex config & tweaks
keras-yolo3 - Training and Detecting Objects with YOLO3
Hekate-Toolbox - A toolbox for Hekate
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/