YOLOv6
PixelLib
| YOLOv6 | PixelLib | |
|---|---|---|
| 11 | 3 | |
| 5,549 | 1,016 | |
| 0.9% | - | |
| 6.7 | 0.0 | |
| about 2 months ago | 7 months ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 only | MIT License |
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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
PixelLib
- YOLOv6: Redefine state-of-the-art for object detection
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To separate objects detected from a video using PixelLib
This is the code, found from the reference here.
- New Project In Computer Vision For Beginner
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
FasterRCNN - Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.
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/
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
keras-yolo3 - Training and Detecting Objects with YOLO3
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
fashion-segmentation - A tensorflow model for segmentation of fashion items out of multiple product images