#objectdetection — Public Fediverse posts

Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm
#Pycharm #Datascience #Tutorials #Computervision #Objectdetection #Python #Tensorflow

https://blog.jetbrains.com/pycharm/2026/05/build-a-live-object-detection-app-for-reachy-mini-with-tensorflow-and-pycharm/

Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm
#Pycharm #Datascience #Tutorials #Computervision #Objectdetection #Python #Tensorflow

https://blog.jetbrains.com/pycharm/2026/05/build-a-live-object-detection-app-for-reachy-mini-with-tensorflow-and-pycharm/

Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm
#Pycharm #Datascience #Tutorials #Computervision #Objectdetection #Python #Tensorflow

https://blog.jetbrains.com/pycharm/2026/05/build-a-live-object-detection-app-for-reachy-mini-with-tensorflow-and-pycharm/

Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm
#Pycharm #Datascience #Tutorials #Computervision #Objectdetection #Python #Tensorflow

https://blog.jetbrains.com/pycharm/2026/05/build-a-live-object-detection-app-for-reachy-mini-with-tensorflow-and-pycharm/

Build a Live Object Detection App for the Reachy Mini With TensorFlow and PyCharm
#Pycharm #Datascience #Tutorials #Computervision #Objectdetection #Python #Tensorflow

https://blog.jetbrains.com/pycharm/2026/05/build-a-live-object-detection-app-for-reachy-mini-with-tensorflow-and-pycharm/

Processing nationwide data for Germany’s digital twin

The scale of the Digital Twin Germany project raises a critical operational q…
#Germany #DE #Europe #EU #Europa #aerialmapping #Aerialsurvey #AerialSurveying #AI #artificialintelligence #automation #Cloud #digitaltwin #GeoAI #geospatial #infrastructure #laserscanning #Lidar #Lidarapplications #Lidardataprocessing #mapping #objectdetection #pointcloud #pointcloudclassification #remotesensing #workflow
https://www.europesays.com/germany/10929/

Talk on the discord about how much time it takes to process images with Darknet/YOLO. No need to guess and throw wild speculation -- run any of the built-in Darknet/YOLO tools and it will tell you exactly how long it takes at every step.

loading /home/stephane/nn/driving/set_04_dash/frame_064661.jpg
-> reading image from disk ........... 3.781 milliseconds [1280 x 720 x 3] [78.7 KiB]
-> resizing image to network dims .... 0.383 milliseconds [640 x 352 x 3]
-> using Darknet to predict .......... 2.581 milliseconds [7 objects]
-> using Darknet to annotate image ... 0.071 milliseconds [1280 x 720 x 3]
-> save output image to disk ......... 2.123 milliseconds [84.9 KiB]
-> total time elapsed ................ 9.324 milliseconds [107 FPS]

#Darknet #YOLO #ObjectDetection #NeuralNetwork

Talk on the discord about how much time it takes to process images with Darknet/YOLO. No need to guess and throw wild speculation -- run any of the built-in Darknet/YOLO tools and it will tell you exactly how long it takes at every step.

loading /home/stephane/nn/driving/set_04_dash/frame_064661.jpg
-> reading image from disk ........... 3.781 milliseconds [1280 x 720 x 3] [78.7 KiB]
-> resizing image to network dims .... 0.383 milliseconds [640 x 352 x 3]
-> using Darknet to predict .......... 2.581 milliseconds [7 objects]
-> using Darknet to annotate image ... 0.071 milliseconds [1280 x 720 x 3]
-> save output image to disk ......... 2.123 milliseconds [84.9 KiB]
-> total time elapsed ................ 9.324 milliseconds [107 FPS]

#Darknet #YOLO #ObjectDetection #NeuralNetwork

Talk on the discord about how much time it takes to process images with Darknet/YOLO. No need to guess and throw wild speculation -- run any of the built-in Darknet/YOLO tools and it will tell you exactly how long it takes at every step.

loading /home/stephane/nn/driving/set_04_dash/frame_064661.jpg
-> reading image from disk ........... 3.781 milliseconds [1280 x 720 x 3] [78.7 KiB]
-> resizing image to network dims .... 0.383 milliseconds [640 x 352 x 3]
-> using Darknet to predict .......... 2.581 milliseconds [7 objects]
-> using Darknet to annotate image ... 0.071 milliseconds [1280 x 720 x 3]
-> save output image to disk ......... 2.123 milliseconds [84.9 KiB]
-> total time elapsed ................ 9.324 milliseconds [107 FPS]

#Darknet #YOLO #ObjectDetection #NeuralNetwork

Talk on the discord about how much time it takes to process images with Darknet/YOLO. No need to guess and throw wild speculation -- run any of the built-in Darknet/YOLO tools and it will tell you exactly how long it takes at every step.

loading /home/stephane/nn/driving/set_04_dash/frame_064661.jpg
-> reading image from disk ........... 3.781 milliseconds [1280 x 720 x 3] [78.7 KiB]
-> resizing image to network dims .... 0.383 milliseconds [640 x 352 x 3]
-> using Darknet to predict .......... 2.581 milliseconds [7 objects]
-> using Darknet to annotate image ... 0.071 milliseconds [1280 x 720 x 3]
-> save output image to disk ......... 2.123 milliseconds [84.9 KiB]
-> total time elapsed ................ 9.324 milliseconds [107 FPS]

#Darknet #YOLO #ObjectDetection #NeuralNetwork

Talk on the discord about how much time it takes to process images with Darknet/YOLO. No need to guess and throw wild speculation -- run any of the built-in Darknet/YOLO tools and it will tell you exactly how long it takes at every step.

loading /home/stephane/nn/driving/set_04_dash/frame_064661.jpg
-> reading image from disk ........... 3.781 milliseconds [1280 x 720 x 3] [78.7 KiB]
-> resizing image to network dims .... 0.383 milliseconds [640 x 352 x 3]
-> using Darknet to predict .......... 2.581 milliseconds [7 objects]
-> using Darknet to annotate image ... 0.071 milliseconds [1280 x 720 x 3]
-> save output image to disk ......... 2.123 milliseconds [84.9 KiB]
-> total time elapsed ................ 9.324 milliseconds [107 FPS]

#Darknet #YOLO #ObjectDetection #NeuralNetwork

I don't talk about Darknet/YOLO much anymore on Mastodon. But I maintain the modern Darknet/YOLO repo.

This repo, written in C++ and CUDA, is used to analyze images and video frames to find objects. You train a neural network to identify things you need, and then you give it images or videos to inspect.

Darknet/YOLO is completely free. Uses the Apache 2 license.

The GitHub mirror is here: https://github.com/hank-ai/darknet/tree/v6-dev#table-of-contents

The main repo is here: https://codeberg.org/CCodeRun/darknet/src/branch/v6-dev#table-of-contents

An example image:
#Darknet #YOLO #NeuralNetwork #ObjectDetection

I don't talk about Darknet/YOLO much anymore on Mastodon. But I maintain the modern Darknet/YOLO repo.

This repo, written in C++ and CUDA, is used to analyze images and video frames to find objects. You train a neural network to identify things you need, and then you give it images or videos to inspect.

Darknet/YOLO is completely free. Uses the Apache 2 license.

The GitHub mirror is here: https://github.com/hank-ai/darknet/tree/v6-dev#table-of-contents

The main repo is here: https://codeberg.org/CCodeRun/darknet/src/branch/v6-dev#table-of-contents

An example image:
#Darknet #YOLO #NeuralNetwork #ObjectDetection

I don't talk about Darknet/YOLO much anymore on Mastodon. But I maintain the modern Darknet/YOLO repo.

This repo, written in C++ and CUDA, is used to analyze images and video frames to find objects. You train a neural network to identify things you need, and then you give it images or videos to inspect.

Darknet/YOLO is completely free. Uses the Apache 2 license.

The GitHub mirror is here: https://github.com/hank-ai/darknet/tree/v6-dev#table-of-contents

The main repo is here: https://codeberg.org/CCodeRun/darknet/src/branch/v6-dev#table-of-contents

An example image:
#Darknet #YOLO #NeuralNetwork #ObjectDetection

I don't talk about Darknet/YOLO much anymore on Mastodon. But I maintain the modern Darknet/YOLO repo.

This repo, written in C++ and CUDA, is used to analyze images and video frames to find objects. You train a neural network to identify things you need, and then you give it images or videos to inspect.

Darknet/YOLO is completely free. Uses the Apache 2 license.

The GitHub mirror is here: https://github.com/hank-ai/darknet/tree/v6-dev#table-of-contents

The main repo is here: https://codeberg.org/CCodeRun/darknet/src/branch/v6-dev#table-of-contents

An example image:
#Darknet #YOLO #NeuralNetwork #ObjectDetection

I don't talk about Darknet/YOLO much anymore on Mastodon. But I maintain the modern Darknet/YOLO repo.

This repo, written in C++ and CUDA, is used to analyze images and video frames to find objects. You train a neural network to identify things you need, and then you give it images or videos to inspect.

Darknet/YOLO is completely free. Uses the Apache 2 license.

The GitHub mirror is here: https://github.com/hank-ai/darknet/tree/v6-dev#table-of-contents

The main repo is here: https://codeberg.org/CCodeRun/darknet/src/branch/v6-dev#table-of-contents

An example image:
#Darknet #YOLO #NeuralNetwork #ObjectDetection

A closer look at image annotation in AI systems

Machines need labeled data to understand images. image annotation services provide that structure by marking objects and patterns. This helps AI systems process visual information and deliver more accurate and reliable outcomes.

Know more: https://www.hitechdigital.com/image-annotation-services

#ImageAnnotationServices #DataAnnotationServices #ImageLabeling #ComputerVision #AITrainingData #MachineLearning #ObjectDetection

AI Image Annotation for Detection Models

Structured polygon annotation, 3D cuboids, landmark detection, and semantic segmentation designed for scalable AI training. An image annotation company delivers datasets for computer vision, medical imaging, and object detection systems.

Know More: https://www.hitechdigital.com/image-annotation-services

#ImageAnnotation #AITrainingData #ObjectDetection #MachineLearning #MedicalImagingAI #DataLabeling

What Is Object Detection? A Simple Guide to How AI Sees Objects

Ever wondered how AI recognizes people, cars, or faces in images? This easy guide breaks down object detection, how it works, and where it’s used in daily life. Learn why image annotation services are essential for training reliable AI models.

Know More: https://www.hitechdigital.com/blog/object-detection-guide

#ObjectDetection #AITrainingData #ImageAnnotationServices

Tôi đã đấu vật với AI (Claude) 14 tiếng mỗi ngày. Không thể hạnh phúc hơn.
— Akio Shiki (@ar_akio) 20 tháng 10, 2025

Là kỹ sư AI, tôi giải quyết "thảm kịch nước ấm" bằng ESP32 và SAM 3! Dù chai trong suốt, kệ kính, ánh sáng phức tạp – hệ thống nhận diện vẫn hoạt động chính xác, không cần hiệu chỉnh. Chứng minh SAM 3 mạnh mẽ trong nhận dạng vật thể trong suốt – tiềm năng cho robot công nghiệp & xe tự hành.

#SmartFridge #IoT #ComputerVision #AI #SAM3 #ESP32 #ObjectDetection #TríTuệNhânTạo

Tôi đã đấu vật với AI (Claude) 14 tiếng mỗi ngày. Không thể hạnh phúc hơn.
— Akio Shiki (@ar_akio) 20 tháng 10, 2025

Là kỹ sư AI, tôi giải quyết "thảm kịch nước ấm" bằng ESP32 và SAM 3! Dù chai trong suốt, kệ kính, ánh sáng phức tạp – hệ thống nhận diện vẫn hoạt động chính xác, không cần hiệu chỉnh. Chứng minh SAM 3 mạnh mẽ trong nhận dạng vật thể trong suốt – tiềm năng cho robot công nghiệp & xe tự hành.

#SmartFridge #IoT #ComputerVision #AI #SAM3 #ESP32 #ObjectDetection #TríTuệNhânTạo

Top 10 Image Annotation Services Transforming Computer Vision in 2026

Explore the leading Image Annotation Services transforming AI in 2026. These top providers offer expert labeling for object detection, segmentation, and classification, helping build robust computer vision models across industries like healthcare, autonomous driving.

Know More: https://telegra.ph/Top-10-Image-Annotation-Services-Shaping-Computer-Vision-in-2026-12-08

#imageannotation #datalabeling #imagesegmentation #objectdetection #techtrends2026

Thinking it is time to release Darknet v5.1. The "Christmas 2025" edition? #Darknet #YOLO #ObjectDetection

Thinking it is time to release Darknet v5.1. The "Christmas 2025" edition? #Darknet #YOLO #ObjectDetection

Thinking it is time to release Darknet v5.1. The "Christmas 2025" edition? #Darknet #YOLO #ObjectDetection

Thinking it is time to release Darknet v5.1. The "Christmas 2025" edition? #Darknet #YOLO #ObjectDetection

Thinking it is time to release Darknet v5.1. The "Christmas 2025" edition? #Darknet #YOLO #ObjectDetection

Why DETRs are replacing YOLOs for real-time object detection

https://blog.datameister.ai/detection-transformers-real-time-object-detection

#HackerNews #DETRs #YOLOs #ObjectDetection #RealTime #AI #MachineLearning

Why DETRs are replacing YOLOs for real-time object detection

https://blog.datameister.ai/detection-transformers-real-time-object-detection

#HackerNews #DETRs #YOLOs #ObjectDetection #RealTime #AI #MachineLearning

Why DETRs are replacing YOLOs for real-time object detection

https://blog.datameister.ai/detection-transformers-real-time-object-detection

#HackerNews #DETRs #YOLOs #ObjectDetection #RealTime #AI #MachineLearning

Why DETRs are replacing YOLOs for real-time object detection

https://blog.datameister.ai/detection-transformers-real-time-object-detection

#HackerNews #DETRs #YOLOs #ObjectDetection #RealTime #AI #MachineLearning

Why DETRs are replacing YOLOs for real-time object detection

https://blog.datameister.ai/detection-transformers-real-time-object-detection

#HackerNews #DETRs #YOLOs #ObjectDetection #RealTime #AI #MachineLearning

Open-source tool automates high-volume image cropping for advertisers: Developer releases Python tool combining YOLO, DETR, and RT-DETR object detection models to process thousands of advertising images without human intervention. https://ppc.land/open-source-tool-automates-high-volume-image-cropping-for-advertisers/ #OpenSource #ImageCropping #Advertising #Python #ObjectDetection

Open-source tool automates high-volume image cropping for advertisers: Developer releases Python tool combining YOLO, DETR, and RT-DETR object detection models to process thousands of advertising images without human intervention. https://ppc.land/open-source-tool-automates-high-volume-image-cropping-for-advertisers/ #OpenSource #ImageCropping #Advertising #Python #ObjectDetection

Open-source tool automates high-volume image cropping for advertisers: Developer releases Python tool combining YOLO, DETR, and RT-DETR object detection models to process thousands of advertising images without human intervention. https://ppc.land/open-source-tool-automates-high-volume-image-cropping-for-advertisers/ #OpenSource #ImageCropping #Advertising #Python #ObjectDetection

YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels. https://hackernoon.com/need-precision-plankton-counts-why-yolov5-shines-but-yolov8-adapts #objectdetection

YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels. https://hackernoon.com/need-precision-plankton-counts-why-yolov5-shines-but-yolov8-adapts #objectdetection

YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels. https://hackernoon.com/need-precision-plankton-counts-why-yolov5-shines-but-yolov8-adapts #objectdetection

YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels. https://hackernoon.com/need-precision-plankton-counts-why-yolov5-shines-but-yolov8-adapts #objectdetection

YOLOv5 hits 97% precision on zooplankton; YOLOv8’s DFL handles class imbalance, boosting excrement hits despite scant labels. https://hackernoon.com/need-precision-plankton-counts-why-yolov5-shines-but-yolov8-adapts #objectdetection

Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training. https://hackernoon.com/train-yolo-in-10-epochs-a-lean-recipe-for-marine-micro-object-detection #objectdetection

Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training. https://hackernoon.com/train-yolo-in-10-epochs-a-lean-recipe-for-marine-micro-object-detection #objectdetection

Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training. https://hackernoon.com/train-yolo-in-10-epochs-a-lean-recipe-for-marine-micro-object-detection #objectdetection

Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training. https://hackernoon.com/train-yolo-in-10-epochs-a-lean-recipe-for-marine-micro-object-detection #objectdetection

Class imbalance (80% Artemia) and SSIM/MSE checks ensure quality across 50 mg, 100 mg, and control images before YOLO training. https://hackernoon.com/train-yolo-in-10-epochs-a-lean-recipe-for-marine-micro-object-detection #objectdetection

YOLOv8 research tutorial: Discover 10 powerful steps to customize YOLOv8 for your research. Boost your AI projects today! #YOLOv8 #ObjectDetection #Research #AI #DeepLearning

https://teguhteja.id/yolov8-research-tutorial-10-step-guide/?utm_source=mastodon&utm_medium=jetpack_social

YOLOv8 research tutorial: Discover 10 powerful steps to customize YOLOv8 for your research. Boost your AI projects today! #YOLOv8 #ObjectDetection #Research #AI #DeepLearning

https://teguhteja.id/yolov8-research-tutorial-10-step-guide/?utm_source=mastodon&utm_medium=jetpack_social

YOLOv8 research tutorial: Discover 10 powerful steps to customize YOLOv8 for your research. Boost your AI projects today! #YOLOv8 #ObjectDetection #Research #AI #DeepLearning

https://teguhteja.id/yolov8-research-tutorial-10-step-guide/?utm_source=mastodon&utm_medium=jetpack_social

YOLOv8 research tutorial: Discover 10 powerful steps to customize YOLOv8 for your research. Boost your AI projects today! #YOLOv8 #ObjectDetection #Research #AI #DeepLearning

https://teguhteja.id/yolov8-research-tutorial-10-step-guide/?utm_source=mastodon&utm_medium=jetpack_social

YOLOv8 research tutorial: Discover 10 powerful steps to customize YOLOv8 for your research. Boost your AI projects today! #YOLOv8 #ObjectDetection #Research #AI #DeepLearning

https://teguhteja.id/yolov8-research-tutorial-10-step-guide/?utm_source=mastodon&utm_medium=jetpack_social