bonito
yolov5
bonito | yolov5 | |
---|---|---|
8 | 129 | |
372 | 47,071 | |
0.0% | 1.8% | |
7.3 | 8.8 | |
5 months ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
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.
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bonito
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miRNA Detection
There is a technology called Nanopore. I’ve never used it myself but the concept was to be able to sequence samples of nucleic acid out in the field. A quick pubmed search indicated that it can detect miRNA, but maybe with some modifications. https://nanoporetech.com Best of luck with it!
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Oxford University Press’s new logo is unfathomably bad
> An unpopular opinion: there are too many other logos that look like the original logo
There are also too many logos that look like the new logo. The first blue circle logos that spring to mind are Blue Circle Cement/Tarmac/Lafarge [1] and Oxford Nanopore [2].
[1]: https://en.wikipedia.org/wiki/Tarmac_(company)
[2]: https://nanoporetech.com/
- PORTABLE DNA SEQUENCER!!!!
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Ask HN: Who is hiring? (May 2022)
Oxford Nanopore Technologies (https://nanoporetech.com/) | Front end developer | Full-time | Oxford | Remote (UK)
Oxford Nanopore Technologies is headquartered at the Oxford Science Park outside Oxford, UK, with satellite offices and commercial presence in many global locations across the US, APAC and Europe. Our DNA/RNA sequencing platform is the only technology that offers real-time analysis (for rapid insights), in fully scalable formats from pocket to population scale. Our goal is to enable the analysis of any living thing, by anyone, anywhere.
Tech stack: Electron, Stencil, React, Typescript, RxJS, GRPC
For more details, please email: [email protected]
- El primer genoma completo de un ser humano abre una nueva era en la ciencia
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Buying artificial membranes
Ok this isn't really my area but I know that there are labs/companies performing these types of electrical current disturbance measurements of membrane-type proteins for both DNA sequencing (https://nanoporetech.com/) and protein sequencing (https://www.nature.com/articles/s41587-019-0401-y).
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ELI5: Why home blood tests do not exist, while we can measure our sugar levels with personal devices at home?
Now nanopore sequencing is solid state and gets much longer reads. https://nanoporetech.com/ and https://en.wikipedia.org/wiki/Nanopore_sequencing
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Raw nanowire sequencer data
Also best of luck with the basecaller, I will say that the latest guppy versions are very good, both in terms of accuracy and speed, as they are GPU accelerated and the best I've seen in accuracy. You may also be interested in Bonito, a tool to generate your own GPU basecalling model or tweak existing models to your data. https://github.com/nanoporetech/bonito.
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?