yolov5
DarkMark
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yolov5 | DarkMark | |
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129 | 8 | |
46,738 | 143 | |
2.6% | - | |
8.9 | 6.9 | |
about 20 hours ago | about 1 month ago | |
Python | C++ | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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yolov5
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จำแนกสายพันธ์ุหมากับแมวง่ายๆด้วยYoLoV5
Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data
- How would i go about having YOLO v5 return me a list from left to right of all detected objects in an image?
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Building a Drowsiness Detection Web App from scratch - pt2
!git clone https://github.com/ultralytics/yolov5.git ## Navigate to the model %cd yolov5/ ## Install requirements !pip install -r requirements.txt ## Download the YOLOv5 model !wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt
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[Help: Project] Transfer Learning on YOLOv8
Specifically what I did was take the coco128.yaml, added 6 new classes from Dataset A (which have already been converted to YOLO Darknet TXT), from index 0-5 and subsequently adjusted the indices of the other COCO classes. The I proceeded to train and validate on Dataset A for 20 epochs.
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Changing labels of default YOLOv5 model
I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?
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First time working with computer vision, need help figuring out a problem in my model
You should add them without annotations. Go through this.
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AI Camera?
You are correct and if you check the firmware, it's yet another famous 3rd party project without attribution, namely https://github.com/ultralytics/yolov5
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First non-default print on K1 - success
On one side, being a Linux user for 24 years now, it annoys me that they rip off code and claiming it as theirs again, thus violating licenses, but on the other thanks to k3d's exploit I'm able to tinker more with the machine and if needed do (selective) updates by hand then with a closed source system. It's not just "klipper", with klipper, fluidd and moonraker, it's also ffmpeg and mjpegstreamer. It's gonna be interesting since they also use a project that isn't just GPL, but APGL (in short "If your software gives service online, you have to publish the source code of it and any library that it borrows functions from.") - they use yolov5 (for AI).
- How does the background class work in object detection?
DarkMark
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Using YOLO for annotation in CVAT
Also see DarkMark. For several years it has had support for loading custom Darknet/YOLO weights (not just MSCOCO!) to help annotate more images. https://www.ccoderun.ca/darkmark/Summary.html
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[Discussion] YOLOv5 training questions, specificaly re-training best practices
You should look at DarkMark. I wrote it specifically to do what you describe. It is an annotation tool that loads the Darknet/YOLO weights, so it can assist in annotating images. I annotate a few images and train, reload DarkMark to annotate some more, train, rinse, lather, repeat.
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When to use YOLOv5 and when not to use the model?
Disclaimer: I'm the author of DarkHelp (the C++ library for Darknet) and DarkMark (the annotation and project management tool for Darknet).
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Annotate data for tracking
If using Darknet/YOLO, look up DarkMark which does have support for video, as well as loading existing neural networks to help annotate images (or video frames) faster. Some info on getting started: https://www.ccoderun.ca/programming/darknet\_faq/#how\_to\_get\_started
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Free AI assisted image labelling tool
You can find DarkMark here: https://github.com/stephanecharette/DarkMark
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Reduce false positive in object detection
Disclaimer: I'm the author of DarkHelp and DarkMark, and I run the Darknet/YOLO discord.
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Extracting Images from Video
I use DarkMark's video import functionality to extract video frames. See this screenshot: https://www.ccoderun.ca/darkmark/Summary.html#DarkMarkImportVideoFrames
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Annotating and detecting objects in a video
DarkMark will extract frames from a video (lots of options, either all frames, sequences of frames, random number of frames, png vs jpeg, resize frames, ...) and then will let you annotate them as you normally would. https://github.com/stephanecharette/DarkMark
What are some alternatives?
mmdetection - OpenMMLab Detection Toolbox and Benchmark
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
django-labeller - An image labelling tool for creating segmentation data sets, for Django and Flask.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
VIAME - Video and Image Analytics for Multiple Environments
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
DarkHelp - C++ wrapper library for Darknet
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
OpenCV - Open Source Computer Vision Library
DarkPlate - License plate parsing using Darknet and YOLO