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#ObjectDetection

1 post1 participant0 posts today

Distracted.

Instead of working on the Darknet configuration code I meant to work on today, I modified the support discord server, and implemented a bunch of new functionality in the Darknet/YOLO Bot.

See the results here: discord.gg/zSq8rtW

Interact with the bot by typing commands such as "help", "repo", "faq", and "kick me".

DiscordJoin the Darknet/YOLO Object Detection Discord Server!Darknet/YOLO object detection framework | 7137 members

I've been facing many issues with using #Poetry (#pythonpoetry) with my #Python based #objectdetection project. I love Poetry for publishing packages, but think that #conda would be better since I have to deal with #CUDA and whatnot. Anyone familiar with a way to use `pyproject.toml` for publishing and building packages, even if Poetry isn't being used for dependency management?

For context, here's the project I'm working on: github.com/slashtechno/wyzely-

GitHubGitHub - slashtechno/wyzely-detect: Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devicesRecognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices - GitHub - slashtechno/wyzely-detect: Recognize faces/objects in a video stream...

Hey #DH #CSS ppl! What is your favorite platform/method of generating labeled training data from images? Have about ten categories that I need to annotate and hoping to have at least 100 examples for each category. Also the dataset is historic Newspaper indices (so lots of weird tables and numbers)

I've known ppl who have used Makesense makesense.ai/ previously and also very much considering taking the plunge and buying a Prodigy prodi.gy/ license. So any advice would be appreciated!

www.makesense.aiMake Sense

✨ Diffusion meets Object Detection

📰 DiffusionDet: Diffusion Model for Object Detection

• It formulates object detection as a denoising diffusion process
from noisy boxes to object boxes

• At training, object boxes diffuse from ground-truth boxes to random distribution

• The model learns to reverse this noising process

• During inference, the model gradually refines a set of randomly generated boxes to refine the results