AYolov2
YOLOv6
| AYolov2 | YOLOv6 | |
|---|---|---|
| 3 | 11 | |
| 156 | 5,549 | |
| 1.9% | 0.8% | |
| 0.0 | 6.7 | |
| over 1 year ago | about 2 months ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 only | 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.
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.
AYolov2
YOLOv6
-
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
-
DeepSort with PyTorch(support yolo series)
meituan/YOLOv6
-
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
-
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
-
[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?
pytorch-efficientdet-api - A PyTorch EfficientDet API for easy training and inference on custom datasets.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
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/
keras-yolo3 - Training and Detecting Objects with YOLO3
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.