🪰🌺Fly pollinator foraging in boreal forests is shaped by climate, forest structure and flower resources
vist.ly/4ap62
#CanopyOpenness #ClimateChange #ClimaticRefugia #ConvolutionalNeuralNetwork #DeepLearning #Diptera #ForestComposition #PollinatorConservation #YOLOv5
vist.ly/4ap62
#CanopyOpenness #ClimateChange #ClimaticRefugia #ConvolutionalNeuralNetwork #DeepLearning #Diptera #ForestComposition #PollinatorConservation #YOLOv5
October 16, 2025 at 5:06 PM
🪰🌺Fly pollinator foraging in boreal forests is shaped by climate, forest structure and flower resources
vist.ly/4ap62
#CanopyOpenness #ClimateChange #ClimaticRefugia #ConvolutionalNeuralNetwork #DeepLearning #Diptera #ForestComposition #PollinatorConservation #YOLOv5
vist.ly/4ap62
#CanopyOpenness #ClimateChange #ClimaticRefugia #ConvolutionalNeuralNetwork #DeepLearning #Diptera #ForestComposition #PollinatorConservation #YOLOv5
📖Published📖
Serra-Marin et al. develop an Automated Camera System (ACS) that integrates Raspberry Pi hardware with YOLOv5, to detect pollinators in plant communities 🐝 🌱 Read more here 🌍 🧪 👇
Serra-Marin et al. develop an Automated Camera System (ACS) that integrates Raspberry Pi hardware with YOLOv5, to detect pollinators in plant communities 🐝 🌱 Read more here 🌍 🧪 👇
buff.ly
November 19, 2025 at 12:02 PM
📖Published📖
Serra-Marin et al. develop an Automated Camera System (ACS) that integrates Raspberry Pi hardware with YOLOv5, to detect pollinators in plant communities 🐝 🌱 Read more here 🌍 🧪 👇
Serra-Marin et al. develop an Automated Camera System (ACS) that integrates Raspberry Pi hardware with YOLOv5, to detect pollinators in plant communities 🐝 🌱 Read more here 🌍 🧪 👇
[EN] How to Use a Flask-Based YOLOv5 Model Repository with ECR and AWS App Runner
https://tech.ldas.jp/en/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
https://tech.ldas.jp/en/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
How to Use a Flask-Based YOLOv5 Model Repository with ECR and AWS App Runner
How to use a Flask-based YOLOv5 model repository with ECR and AWS App Runner
tech.ldas.jp
May 23, 2026 at 12:15 PM
[EN] How to Use a Flask-Based YOLOv5 Model Repository with ECR and AWS App Runner
https://tech.ldas.jp/en/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
https://tech.ldas.jp/en/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
Once in a conference i saw people using Yolo to track a pattern of four big green dots on a red background in camera images ! And one question from the audience was "yolov5 or yolov6 ?" ...
December 19, 2024 at 9:47 PM
Once in a conference i saw people using Yolo to track a pattern of four big green dots on a red background in camera images ! And one question from the audience was "yolov5 or yolov6 ?" ...
the fly can play Drag'n Wash
I've successfully trained the MaleCNS v1.0 fruit fly connectdome's 166,700 neurons using YOLOv5 and video pretraining data to play drag'n wash in a keyboard environment with attention-based aiming.
The fly has found the most success on Ryan.
I've successfully trained the MaleCNS v1.0 fruit fly connectdome's 166,700 neurons using YOLOv5 and video pretraining data to play drag'n wash in a keyboard environment with attention-based aiming.
The fly has found the most success on Ryan.
September 17, 2026 at 4:12 PM
the fly can play Drag'n Wash
I've successfully trained the MaleCNS v1.0 fruit fly connectdome's 166,700 neurons using YOLOv5 and video pretraining data to play drag'n wash in a keyboard environment with attention-based aiming.
The fly has found the most success on Ryan.
I've successfully trained the MaleCNS v1.0 fruit fly connectdome's 166,700 neurons using YOLOv5 and video pretraining data to play drag'n wash in a keyboard environment with attention-based aiming.
The fly has found the most success on Ryan.
📃Scientific paper: Doppler-Enhanced Deep Learning: Improving Thyroid Nodule Segmentation with YOLOv5 Instance Segmentation
Ref.: arXiv, 2025
➡️ Continued on ES/IODE
Ref.: arXiv, 2025
➡️ Continued on ES/IODE
September 28, 2026 at 3:00 PM
📃Scientific paper: Doppler-Enhanced Deep Learning: Improving Thyroid Nodule Segmentation with YOLOv5 Instance Segmentation
Ref.: arXiv, 2025
➡️ Continued on ES/IODE
Ref.: arXiv, 2025
➡️ Continued on ES/IODE
Using 198 Kigali street images, researchers compared YOLOv5, Faster R‑CNN, SSD and RetinaNet for motorbike detection. YOLOv5 was fastest; Faster R‑CNN gave the best localization. https://getnews.me/yolov5-faster-r-cnn-ssd-retinanet-motorbike-detection-in-kigali/ #motorbikedetection #kigali
October 8, 2025 at 5:46 AM
Using 198 Kigali street images, researchers compared YOLOv5, Faster R‑CNN, SSD and RetinaNet for motorbike detection. YOLOv5 was fastest; Faster R‑CNN gave the best localization. https://getnews.me/yolov5-faster-r-cnn-ssd-retinanet-motorbike-detection-in-kigali/ #motorbikedetection #kigali
[EN] Building an Object Detection API Using AWS Lambda (Flask + YOLOv5)
https://tech.ldas.jp/en/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
https://tech.ldas.jp/en/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
Building an Object Detection API Using AWS Lambda (Flask + YOLOv5)
Building an Object Detection API Using AWS Lambda (Flask + YOLOv5)
tech.ldas.jp
June 13, 2026 at 12:30 PM
[EN] Building an Object Detection API Using AWS Lambda (Flask + YOLOv5)
https://tech.ldas.jp/en/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
https://tech.ldas.jp/en/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
Tests in blurry microfluidic images show YOLOv8 more adaptable, while YOLOv5 may need task-specific tweaks for hard marine debris. #computervision
Comparative Study of YOLOv5 and YOLOv8 for Challenging Aquatic Object Detection
hackernoon.com
September 9, 2025 at 10:24 AM
Tests in blurry microfluidic images show YOLOv8 more adaptable, while YOLOv5 may need task-specific tweaks for hard marine debris. #computervision
How AI can support animal behavioural studies 💡
Have a look at how we tracked the bumblebees' flight paths with AI on a flowering Lotus creticus patch. 👀🐝
@pozsgaig.bsky.social
#Yolov5 #Norfair #Bombusterrestris #Beestudies #InsectAI
➡️ www.youtube.com/watch?v=p2JZ...
Have a look at how we tracked the bumblebees' flight paths with AI on a flowering Lotus creticus patch. 👀🐝
@pozsgaig.bsky.social
#Yolov5 #Norfair #Bombusterrestris #Beestudies #InsectAI
➡️ www.youtube.com/watch?v=p2JZ...
Tracking the bumblebees' flight paths with AI
YouTube video by Zsófia Varga-Szilay
www.youtube.com
April 21, 2025 at 10:27 AM
How AI can support animal behavioural studies 💡
Have a look at how we tracked the bumblebees' flight paths with AI on a flowering Lotus creticus patch. 👀🐝
@pozsgaig.bsky.social
#Yolov5 #Norfair #Bombusterrestris #Beestudies #InsectAI
➡️ www.youtube.com/watch?v=p2JZ...
Have a look at how we tracked the bumblebees' flight paths with AI on a flowering Lotus creticus patch. 👀🐝
@pozsgaig.bsky.social
#Yolov5 #Norfair #Bombusterrestris #Beestudies #InsectAI
➡️ www.youtube.com/watch?v=p2JZ...
AWS Lambdaを用いた物体検出API（Flask + yolov5）の構築
https://tech.ldas.jp/ja/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
https://tech.ldas.jp/ja/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
AWS Lambdaを用いた物体検出API（Flask + yolov5）の構築
AWS Lambdaを用いた物体検出API（Flask + yolov5）の構築
tech.ldas.jp
June 15, 2026 at 10:06 AM
AWS Lambdaを用いた物体検出API（Flask + yolov5）の構築
https://tech.ldas.jp/ja/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
https://tech.ldas.jp/ja/posts/building-an-object-detection-api-using-aws-lambda/
#aws #flask #python
Flaskを用いてyolo5モデルを公開するリポジトリのECRとAWS App Runnerでの使用方法
https://tech.ldas.jp/ja/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
https://tech.ldas.jp/ja/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
Flaskを用いてyolo5モデルを公開するリポジトリのECRとAWS App Runnerでの使用方法
Flaskを用いてyolo5モデルを公開するリポジトリのECRとAWS App Runnerでの使用方法
tech.ldas.jp
May 25, 2026 at 9:18 AM
Flaskを用いてyolo5モデルを公開するリポジトリのECRとAWS App Runnerでの使用方法
https://tech.ldas.jp/ja/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
https://tech.ldas.jp/ja/posts/how-to-use-a-flask-based-yolov5-model-repository/
#aws #flask #machinelearning
🚀 Just completed a full pipeline for YOLOv5 on the #RK3568!
From local training on Windows to real-time NPU inference on the dev board.
✅ YOLOv5 Custom Training ✅ .pt ➡️ .onnx ➡️ .rknn conversion ✅ RKNN-Toolkit2 & RKNPU2 optimization ✅ Successful board-level deployment
www.forlinx.net/industrial-n...
From local training on Windows to real-time NPU inference on the dev board.
✅ YOLOv5 Custom Training ✅ .pt ➡️ .onnx ➡️ .rknn conversion ✅ RKNN-Toolkit2 & RKNPU2 optimization ✅ Successful board-level deployment
www.forlinx.net/industrial-n...
Train YOLOv5 and Deploy to RK3568: A Step-by-Step RKNN Conversion Guide - Blog - Forlinx Embedded Technology Co., Ltd.
Learn how to train a custom YOLOv5 model, convert .pt to RKNN, and deploy on RK3568. Covers environment setup, LabelImg, ONNX export, and rknpu2 runtime deployment.
www.forlinx.net
February 11, 2026 at 3:42 AM
🚀 Just completed a full pipeline for YOLOv5 on the #RK3568!
From local training on Windows to real-time NPU inference on the dev board.
✅ YOLOv5 Custom Training ✅ .pt ➡️ .onnx ➡️ .rknn conversion ✅ RKNN-Toolkit2 & RKNPU2 optimization ✅ Successful board-level deployment
www.forlinx.net/industrial-n...
From local training on Windows to real-time NPU inference on the dev board.
✅ YOLOv5 Custom Training ✅ .pt ➡️ .onnx ➡️ .rknn conversion ✅ RKNN-Toolkit2 & RKNPU2 optimization ✅ Successful board-level deployment
www.forlinx.net/industrial-n...
Advancing e-waste classification with customizable YOLO based deep learning models
->Nature | #MachineLearning | More info from EcoSearch
->Nature | #MachineLearning | More info from EcoSearch
Advancing e-waste classification with customizable YOLO based deep learning models
This section presents the results obtained from the models improved – YOLOv5, YOLOv7, and YOLOv8. YOLOv5 results The classification matrix from Fig. 11 compares the true labels (horizontal axis) against the predicted labels (vertical axis) for various electronic components and...
www.nature.com
May 25, 2025 at 8:35 AM
Mira, alguien ya está haciendo conteos de abejas en entradas de colmenas....
youtu.be/qZW5eYd0Yw8?...
youtu.be/qZW5eYd0Yw8?...
YOLOv5 Honey Bee Detection Model - 1hr Sample Footage
YouTube video by Matt Nudi
youtu.be
October 23, 2025 at 11:55 PM
Mira, alguien ya está haciendo conteos de abejas en entradas de colmenas....
youtu.be/qZW5eYd0Yw8?...
youtu.be/qZW5eYd0Yw8?...
Xiangjie Luo, Bo Shao, Zhihao Cai, Yingxun Wang
A lightweight YOLOv5-FFM model for occlusion pedestrian detection
https://arxiv.org/abs/2408.06633
A lightweight YOLOv5-FFM model for occlusion pedestrian detection
https://arxiv.org/abs/2408.06633
August 14, 2024 at 6:02 AM
Xiangjie Luo, Bo Shao, Zhihao Cai, Yingxun Wang
A lightweight YOLOv5-FFM model for occlusion pedestrian detection
https://arxiv.org/abs/2408.06633
A lightweight YOLOv5-FFM model for occlusion pedestrian detection
https://arxiv.org/abs/2408.06633
(PDF, 2023) Delivering sensory and semantic visual information via auditory feedback on mobile technology apps.dtic.mil/sti/trecms/p... #AI-Sight app, #SoundSight, #blindness #iPhone #LiDAR #DeepLabV3 #YOLOv5 #YOLOv8
May 4, 2026 at 5:34 PM
(PDF, 2023) Delivering sensory and semantic visual information via auditory feedback on mobile technology apps.dtic.mil/sti/trecms/p... #AI-Sight app, #SoundSight, #blindness #iPhone #LiDAR #DeepLabV3 #YOLOv5 #YOLOv8
Junjia Qin, Kangli Xu
Design and Implementation of Automatic Assisted Aiming System For Robomaster EP Based on YOLOv5
https://arxiv.org/abs/2312.05055
Design and Implementation of Automatic Assisted Aiming System For Robomaster EP Based on YOLOv5
https://arxiv.org/abs/2312.05055
July 25, 2024 at 6:01 AM
Junjia Qin, Kangli Xu
Design and Implementation of Automatic Assisted Aiming System For Robomaster EP Based on YOLOv5
https://arxiv.org/abs/2312.05055
Design and Implementation of Automatic Assisted Aiming System For Robomaster EP Based on YOLOv5
https://arxiv.org/abs/2312.05055
Exciting to see advancements with YOLOv9! The evolution from YOLOv5 to YOLOv9 has significant implications for real-time object detection in robotics, especially with improvements in speed and accuracy. Can't wait to explore its impact on autonomous systems and smart robotics! 🎉
March 26, 2026 at 11:59 AM
Exciting to see advancements with YOLOv9! The evolution from YOLOv5 to YOLOv9 has significant implications for real-time object detection in robotics, especially with improvements in speed and accuracy. Can't wait to explore its impact on autonomous systems and smart robotics! 🎉
(Note for anyone double-checking the date as they read this, the LinkedIn post that Leo is linking to here was from a week ago, even though YOLOv5 and CV2 sound like parts of a joke)
April 1, 2025 at 9:33 AM
(Note for anyone double-checking the date as they read this, the LinkedIn post that Leo is linking to here was from a week ago, even though YOLOv5 and CV2 sound like parts of a joke)
images and CV techniques, where transfer learning was applied to fine-tune the You Only Look Once version 5 (YOLOv5) model. The developed model ultimately achieved satisfactory performance, with precision, recall, mAP50, and mAP50 values approaching [4/5 of https://arxiv.org/abs/2505.08336v1]
May 14, 2025 at 6:01 AM
images and CV techniques, where transfer learning was applied to fine-tune the You Only Look Once version 5 (YOLOv5) model. The developed model ultimately achieved satisfactory performance, with precision, recall, mAP50, and mAP50 values approaching [4/5 of https://arxiv.org/abs/2505.08336v1]
fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 [4/6 of https://arxiv.org/abs/2503.07115v1]
March 11, 2025 at 7:35 AM
fused feature map is processed through an enhanced backbone and detection head based on the YOLOv5 framework to achieve accurate detection results. To validate our method, we propose a new dataset, named ARD100, which comprises 100 videos (202,467 [4/6 of https://arxiv.org/abs/2503.07115v1]
Ever wondered how to spot ships from space, even through clouds? This tutorial uses Umbra Space's open SAR data and YOLOv5 to identify vessels with impressive accuracy. Check it out - it's pretty fabulous! tech.marksblogg.com/yolo-umbra-s... #remotesensing #shipdetection #ai
December 9, 2024 at 2:04 PM
Ever wondered how to spot ships from space, even through clouds? This tutorial uses Umbra Space's open SAR data and YOLOv5 to identify vessels with impressive accuracy. Check it out - it's pretty fabulous! tech.marksblogg.com/yolo-umbra-s... #remotesensing #shipdetection #ai
These improvements led to state-of-the-art performance in terms of accuracy and speed. Some of the major improvements in YOLOv5 include mosaic data augmentation and auto learning bounding box anchors.
July 12, 2023 at 1:58 PM
These improvements led to state-of-the-art performance in terms of accuracy and speed. Some of the major improvements in YOLOv5 include mosaic data augmentation and auto learning bounding box anchors.
🤖 Watch the Raspberry Pi AI Hat + run real-time demos:
🔍 Object Detection (YOLOv5–v8)
🧍♂️ Pose Estimation
🧠 Segmentation & Depth
All powered by Hailo-15 on Pi 5!
Full demo video 👉 youtu.be/n9be3g5lv9c
#RaspberryPi #AI #YOLO #EdgeAI #MachineLearning
🔍 Object Detection (YOLOv5–v8)
🧍♂️ Pose Estimation
🧠 Segmentation & Depth
All powered by Hailo-15 on Pi 5!
Full demo video 👉 youtu.be/n9be3g5lv9c
#RaspberryPi #AI #YOLO #EdgeAI #MachineLearning
Running AI Demos on the Raspberry Pi AI Hat + | Object Detection, Pose, Segmentation, Depth
YouTube video by Caroline Dunn
youtu.be
July 23, 2025 at 12:06 PM
🤖 Watch the Raspberry Pi AI Hat + run real-time demos:
🔍 Object Detection (YOLOv5–v8)
🧍♂️ Pose Estimation
🧠 Segmentation & Depth
All powered by Hailo-15 on Pi 5!
Full demo video 👉 youtu.be/n9be3g5lv9c
#RaspberryPi #AI #YOLO #EdgeAI #MachineLearning
🔍 Object Detection (YOLOv5–v8)
🧍♂️ Pose Estimation
🧠 Segmentation & Depth
All powered by Hailo-15 on Pi 5!
Full demo video 👉 youtu.be/n9be3g5lv9c
#RaspberryPi #AI #YOLO #EdgeAI #MachineLearning