Yolo v4

Yolo v4 смотреть последние обновления за сегодня на .

Yolov4 Object Detection - How it Works & Why it's So Amazing! | OpenCV Python | Computer Vision

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Enroll now in YOLO+ & YOLOv7,R,X,v5,v4,v3 - 81 Seats Left - $19pm 🤍 ~ Want to Learn YOLOv7 and solve real-world problems? 🎯FREE YOLOv7 Nano Course - 🤍 🚀 Full YOLOv7 Course - 🤍 ☕️ Show your appreciation for this tutorial - Please Buy me a Coffee/Chai so I can create more free tutorials for you 😊 - 🤍 Find out what makes YOLOv4 Object Detection — Superior, Faster & More Accurate in Object Detection. This Computer Vision tutorial is based in OpenCV Python Timecode 0:00 - Introduction to yolo v4 object detection 3:19 - Object Detector Architectures 4:13 - Selection of Architecture 5:20 - Training Optimizations 8:02 - Additional Improvements 8:32 - Experimental Setup 10:50 - Results 11:29 - Summary So guess what, YOLOv4 has just been released a few days ago, and I must say I am really really excited by this release. Why? Well Yolo version 3 was quite popular, robust and quick, and now YOLOv4 in comparison I feel is a significant upgrade in terms of speed and performance. So, this article I am going to dissect the paper YOLOv4: Optimal Speed and Accuracy of Object Detection by Alexey Bochkovsky, Chien Yao and Hon-Yuan. Wait — hold it… what happened to the original creators of Yolo v1–3 Joseph Redmon and Ali Farhadi — Well Joseph or Joe tweeted in Feb 2020 that he will stop Computer vision research because of how the technology was being used for military applications and that the privacy concerns were having a societal impact. Okay so back to YOLOv4, I am not going to cover YOLO v2 and Yolo v3 in this video because I already cover it in another video of mine which you can check out on my YouTube Channel. I’ll be dissecting the YOLOv4 paper and help you understand this great technology without too much technical jargon, to uncover: 1)How it works, 2) How it was developed, 3) What approached they used, 4) Why they used particular methods, 5)As well how it performs in comparison to competing object detection models, 6) and Finally, why it’s so awesome! Okay so if you are ready to get started with AI, Computer vision and YOLOv4! 😉 References: 🤍 Learn Advanced Tutorials ►Augmentedstartups.info/Teachable-AI-Bootcamp Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram

2020 YOLOv4 paper summary

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* Paper: 🤍 * 2016 DenseNet: 🤍 * 2017 Sqeeuze and Excitation: 🤍 * 2017 FPN: 🤍 * 2019 CSPNet: 🤍 * 2020 Cross-iteration BN: 🤍 * Slide: 🤍 * LinkedIn: 🤍 Reference: [Backbone] * DenseNet: 🤍 * Cross stage partial connections (CSP): 🤍 * YOLOv3-spp: 🤍 [Bag of Freebies] * SAT: 🤍 * DIoU: 🤍 * CBN: 🤍 [Bag of Specials] * PAN: 🤍 * Squeeze-and-Excitation: 🤍 * Spatial Attention Module: 🤍 * SPP: 🤍 * Mish: 🤍

The YOLOv4 algorithm | Introduction to You Only Look Once, Version 4 | Real Time Object Detection

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This week I cover the new real-time object detection YOLOv4 algorithm! Ask any questions or remarks you have in the comments, I will gladly answer to everything! Introduction to YOLO: 🤍 YOLOv4 paper: 🤍 YOLOv4 code: 🤍 A complete read of YOLOv4: 🤍 Subscribe to not miss any AI news and terms clearly vulgarized! #ObjectDetection #YouOnlyLookOnce #YOLO Share this to someone who needs to learn more about Artificial Intelligence! Spread knowledge, not germs! Join Our Discord channel, Learn AI Together: 🤍 Follow me for more AI content! Instagram: 🤍 LinkedIn: 🤍linkedin.com/in/whats-ai Twitter: 🤍 Facebook: 🤍 The best courses to start and progress in AI: 🤍 Song credit: 🤍

Object detection using YOLO v4 and pre trained model | Deep Learning Tutorial 32 (Tensorflow)

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In this video we will use YOLO V4 and use pretrained weights to detect object boundaries in an image. The model was trained on COCO dataset using YOLO V4. Watch this to understand how yolo algorithm works: 🤍 Windows setup instructions: 🤍 Above, I was getting errors when I used .\build.ps1 command but using following command instead worked: powershell -ExecutionPolicy Bypass -File .\build.ps1 Make sure you are installing a compatible version of CUDA. For me it was CUDA 10.1, when I installed 11.x version I was getting all kind of errors so had to downgrade it to 10.1 Based on your system you might have to use a different version download yolov4.weights from 🤍 COCO labels: 🤍 YOLO research papers YOLO v1: 🤍 YOLO v2: 🤍 YOLO v3: 🤍 Do you want to learn technology from me? Check 🤍 for my affordable video courses. #objectdetectionusingyolo #yoloobjectdetection #yolov4objectdetection #yoloalgorithm #yolov4 #yolodeeplearning 🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin: 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

Object Detection Using YOLO v4 on Custom Dataset | Practical Implementation

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Step by step Implementation of YOLO v4. Dataset Preparation for yolo v4. Train your custom Yolo v4 Model Test your Yolo v4 Model Github Link: 🤍 What is YOLO? YOLO stands for You Only Look Once YOLO is an algorithm that uses neural networks to provide real-time object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals. With the timeline, it has become faster and better, with its versions named as: YOLO V1 YOLO V2 YOLO V3 YOLO V4 YOLO V5 YOLO V2 is better than V1 in terms of accuracy and speed. YOLO V3 is not faster than V2 but is more accurate than V2 and so on. How the YOLO algorithm works? YOLO algorithm works using the following three techniques: 1- Residual blocks: image is divided into various grids. Each grid has a dimension of n X n 2- Bounding box regression 3- Intersection Over Union (IOU) : YOLO uses IOU to provide an output box that surrounds the objects perfectly. Contact: aarohisingla1987🤍gmail.com #yolov4 #yolo #objectdetection #computervision #deeplearning #ai #artificialintelligence #ml #machinelearning #neuralnetworks #darknet

What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)

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YOLO (You only look once) is a state of the art object detection algorithm that has become main method of detecting objects in the field of computer vision. Previously people used techniques such as sliding window object detection, R CNN, Fast R CNN and Faster R CNN. But after its invention in 2015, YOLO has become an industry standard for object detection due to its speed and accuracy. In this video we will understand the theory behind how exactly YOLO algorithm works. In next video we will write code to detect objects using YOLO framework. 🔖 Hashtags 🔖 #yoloalgorithm #yolodeeplearning #yoloobjectdetection #yolopython #yoloobjectdetection #yoloopencv Do you want to learn technology from me? Check 🤍 for my affordable video courses. Deep learning playlist: 🤍 Machine learning playlist : 🤍   🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin: 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

10分鐘開始把玩YOLO v4 ~Hands on YOLO v4 in 10 minutes

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1.此影片教學沒有理論解說,只有一步步帶你快速使用YOLO v4,如果你需要使用該模型進行訓練,建議還是要花時間了解整個模型喔~ 1.Instruct you to play YOLO v4 step by step without much explanation of theories. If you want to train your data set, it's better to understand the details. 2.執行即時偵測時,你會發現我的聲音跟影像無法同步,這是因為我的FPS只有17,但螢幕錄影是FPS=30而造成影像與聲音無法Match~ 2.During executing the real time object detection, my voice and images are not synchronous because the YOLO v4 performance by GTX 1080Ti is only 17 FPS while my screen recording is 30FPS~ 影片資訊相關 Information: Source code: 🤍 相關影片: ★人臉偵測之Dlib教學與使用!!很難Build的USE_CUDA版本的方法也一起教給你 🤍 ★人臉偵測之MTCNN教學與使用(The tutorial of face detection using MTCNN) 🤍 ★人臉偵測哪個好用?傳統算法Dlib?還是深度學習的MTCNN? (Face detection comparison between Dlib and MTCNN 🤍 ★如何使用AI來檢測是否有戴口罩(使用介紹與效能測試) 🤍 ★Face mask detection(使用AI SSD進行口罩偵測)程式碼詳細解說 🤍 ★人工智慧讓影像變得好好玩,介紹5個使用AI人工智慧玩轉影像的網站及APP 🤍 ★Python OpenCV執行Video capture(擷取攝影機串流影像)之程式碼詳細解說 🤍 ★手把手教學快速建置開發AI的環境(WIN10、Anaconda(Python, Tensorflow, CUDA, cuDNN)、Pycharm) 🤍 ★Windows(win10) install Tensorflow(tf2.0↑,tf1.14~1.13 and 1.12↓ coexisting)、CudaToolkit、CUDNN、Pycharm 🤍

YOLOv4 Tutorial #1 - Installation in 10 Steps | OpenCV Python | Computer Vision 2020

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Enroll now in YOLO+ & YOLOv7,R,X,v5,v4,v3 - 81 Seats Left - $19pm 🤍 ~ Want to Learn YOLOv7 and solve real-world problems? 🎯FREE YOLOv7 Nano Course - 🤍 🚀 Full YOLOv7 Course - 🤍 ☕️ Show your appreciation for this tutorial - Please Buy me a Coffee/Chai so I can create more free tutorials for you 😊 - 🤍 Hi guys, in this Computer Vision tutorial you will learn how to install YOLOv4 Object Detection in 10 Steps. I will show you how to set up YOLOv4 Darknet with OpenCV in Python. YOLOR is significantly better than YOLOv4 ⭐YOLO-R Course + Github - 🤍 =This Video is Sponsored by Altium= ⭐Download Altium Designer Here - 🤍 ⭐15 Day FREE Altium Trial - 🤍 So in the last lecture, I spoke about how YOLOv4 works and why its so awesome! Today Im going to show you how to install the main dependencies in 10 Steps. If you follow these steps with me you should be able to get YOLOv4 working on images, videos and webcams in the upcoming tutorials. Let’s go through the 10 steps that we need to for YOLOv4. Once you have completed the steps, in the next video, I will show you how to implement YOLOv4 on images, video and webcam. ►References: 🤍 - This course was produced in partnership with Geeky Bee AI Experts in AI and Deep learning Development ►🤍 ►🤍 ►🤍 Learn Advanced Tutorials on Augmented Startups ►Augmentedstartups.info/Teachable-AI-Bootcamp Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram #YOLOv4 #artificialintelligence #computervision - We teach YOLO v2, YOLO v3 and YOLOv4 Music Credit: Simon & Garfunkel The Sound of Silence (Electric Version) SME Timecode 0:00 0. Introduction 3:47 1. Install Python 5:02 2. Git Installation 5:17 3. CMake Installation 5:43 4. Visual Studio Installation 6:45 5. Updating GPU Driver 7:32 6. CUDA installation 9:05 7. CuDNN Installation 10:53 8. OpenCV Installation 11:51 9. CMake OpenCV Configuration 12:50 10. Building OpenCV in Visual Studio

Yolo v4 vs v5

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Yolov4 vs Yolov5 Runtime Fps

YOLOv4 | Object Detection Using Yolo v4

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In this video, I have explained what is yolo algorithm and how yolo algorithm work and what is new in yolov4 . Practical Implementation of Yolo V4 is: 🤍 What is YOLO? YOLO stands for You Only Look Once YOLO is an algorithm that uses neural networks to provide real-time object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals. With the timeline, it has become faster and better, with its versions named as: YOLO V1 YOLO V2 YOLO V3 YOLO V4 YOLO V5 YOLO V2 is better than V1 in terms of accuracy and speed. YOLO V3 is not faster than V2 but is more accurate than V2 and so on. How the YOLO algorithm works? YOLO algorithm works using the following three techniques: 1- Residual blocks: image is divided into various grids. Each grid has a dimension of n X n 2- Bounding box regression 3- Intersection Over Union (IOU) : YOLO uses IOU to provide an output box that surrounds the objects perfectly. #yolo #objectdetection #yolov4 #yolov3 #ai #artificialintelligence #deeplearning #cnn #convolutionalneuralnetwork #deepneuralnetworks #ml #pifordtechnologies #aarohisingla

YoloV4 ile Object Detection(Nesne Tanıma) Modeli Nasıl Eğitilir ?

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Merhabalar ben Yakup, Bu videoda sizlere yolov4 ile object detection modeli nasıl oluşturabileceğimizi göstermeye çalıştım. Bu iş için google'ın bize sunmuş olduğu Google Collab üzerinden ücretsiz Gpu kullandık. Object detection hakkında hiç bilginiz olmasa bile bu videoyu takip ederek siz de kendi Nesne Tanıma modelinize sahip olabilirsiniz. Google Collab Linki:🤍 YoloV3 ile nesne tanıma modeli eğitimi:🤍 Data set nasıl oluşturulur :🤍 / 🤍

YOLOv4 - The most accurate real-time neural network for object detection

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YOLO v4 source code: 🤍 paper: 🤍 medium: 🤍

Install and run YOLOv4-Darknet on Windows

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This video shows step by step tutorial on how to install and run yolov4-darknet on your local Windows system. ① ⚡⚡ Website Blog post on this ⚡⚡ 👉🏻 🤍 ⚡⚡ Medium post ⚡⚡ 👉🏻 🤍 ② ⚡⚡ Website Blog post on CUDA & cuDNN installation ⚡⚡ 👉🏻 🤍 ⚡⚡ Medium post on CUDA & cuDNN installation ⚡⚡ 👉🏻 🤍 ③ ⚡⚡ Github link for Official Darknet repository ⚡⚡ 👉🏻 🤍 ④ ⚡⚡ CUDA Toolkit download links ⚡⚡ 👉🏻 🤍 👉🏻 🤍 ⑤ ⚡⚡ Latest cuDNN version download ⚡⚡ 👉🏻 🤍 ⚡⚡ cuDNN archive download link ⚡⚡ 👉🏻 🤍 ⑥ ⚡⚡ OpenCV download link ⚡⚡ 👉🏻 🤍 ⑦ ⚡⚡ CUDA compiler Compute Capability(CC) link ⚡⚡ 👉🏻 🤍 ⑧⚡⚡Official yolov4 weights file ⚡⚡ 👉🏻 🤍 ⑨ ⚡⚡ Required Software ⚡⚡ 👉🏻 CMake GUI: 🤍 👉🏻 Microsoft Visual Studio: 🤍 #yolov4 #objectdetection #yolov4onwindows #yolov4darknet ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ GitHub repo with all the code and stuff: 🤍 Follow me on twitter: 🤍 Follow me on insta: 🤍 Contact me directly: ✉ 👉🏻 admin🤍techzizou.com ✉ 👉🏻 support🤍techzizou.com ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Making these videos takes a lot of time and effort, so if you like these videos and if you can, then please support the channel using any of the following: ► Buy me a coffee! ☕ 👉🏻 buymeacoffee.com/techzizou ► Support channel on Patreon! 🖖 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Thanks for watching!

Yolov4 EasyInstall(Yolov4 쉽게 설치)

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현재 시점 최적의 욜로v4 설치 방법입니다. This is the best way to install Yolo v4 at this time. [Resource] 🤍

how to train YOLO v3, v4 for custom objects detection | using colab free GPU

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If you like the video, please subscribe to the channel by using the below link 🤍 Hi Everyone in this video I have explained how to train YOLO v4 for custom object detection on google colab utilizing the free GPU resources. This video is very special because it provides complete overview of changing the make file configuration file and creating training and testing dataset feel free to add your custom class and train your own model. I have also explained how to use trained model to detect objects on live video. 1. Add crome extension to download images by below URL 🤍 2. Download rename files jupyter notebook form below link paste in the same folder where you placed all the images run it all the image files will be renamed 🤍 2.1 Download labelImg tool with below link 🤍 3. git link to clone darknet on colab 🤍 4. Get train and test data generator from here 🤍 Note for point 4 :- I am not the authors for 2 py files complete credit goes to authors for creating-files-data-and-name, creating-train-and-test-txt-files files. 5. Download pre-trained weights for the convolutional layers (154 MB): 🤍 6. command to train the model (take care of single line and spaces) !darknet/darknet detector train custom_data/labelled_data.data darknet/cfg/yolov3_custom.cfg custom_weight/darknet53.conv.74 -dont_show 7. Download code to use trained model to detect object on live video 🤍 In this tutorial I’m going to explain you one of the easiest way to train YOLO to detect a custom object even if you’re a beginner and have no experience with coding. This video cover: 1. Setting up Google Colab as a cloud VM with Free GPU. 2. Commands to get Darknet with YOLOv3 weights installed and running. 3. YOLOv3 pretrained coco model detections in the Cloud. 4. Configuration for Custom YOLOv3 Training in the Cloud. 5. Training Custom YOLOv3 Object Detector in the Cloud.

How to Train YOLO v4 Tiny (Darknet) on a Custom Dataset

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YOLOv4-tiny is smaller version of YOLO v4 that emphasizes speed in model predictions, which is perfect for limited compute environments (even CPUs) like mobile phones or embedded machine learning. This YOLO v4 tiny tutorial breaks down what YOLOv4-tiny is, preparing labeled bounding box data for object detection, training a YOLO v4 tiny Darknet model with free resources on Google Colab on your own dataset, and using that model to perform inference. In this example, we train our example model on infrared thermal images. ✅ Subscribe: 🤍 Tools: Roboflow - 🤍 Public datasets - 🤍 Colab notebook - 🤍 Blog post - 🤍 Create Labeled Data CVAT - 🤍 Follow us on Twitter: 🤍

YOLOv4 in the CLOUD: Build and Train Custom Object Detector (FREE GPU)

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Learn how to train your very own YOLOv4 custom object detector in Google Colab! Get yolov4 built with darknet and running object detections in minutes. Walk-through the steps to gather your own custom dataset, configure YOLOv4 for training, and then train your own custom object detector to detect whatever classes and objects you want. ALL WITH FREE GPU! This tutorial covers it all. #yolov4 #objectdetection #cloud THE GOOGLE COLAB NOTEBOOK: 🤍 In this video I cover: 1. Setting up Google Colab as a Cloud VM with Free GPU. 2. Commands to Build Darknet 3. How to Gather Custom Training and Validation Datasets 4. Configuration for Custom YOLOv4 Training in the Cloud 5. Training Custom YOLOv4 Object Detector 6. Validating Custom Model with mAP 7. Running Custom Model with Detections Resources Github Code Repository: 🤍 Tutorial for YOLOv4 Pre-trained Model, Running on Video, Formatting Output and Detections etc.: 🤍 Generate Open Images Custom Dataset (recommended):🤍 Create Dataset with Manual Annotations: 🤍 The Official YOLOv4 paper: 🤍 If you enjoyed the video, toss it a like! 👍 To Subscribe: 🤍 Thanks so much for watching! - The AI Guy

PR-249: YOLOv4: Optimal Speed and Accuracy of Object Detection

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#YOLOv4 #ObjectDetection #DeepLearning #PR12 안녕하세요, Cognex Deep Learning Lab KR 에서 Research Engineer로 근무하고 있는이호성입니다. PR-12 논문 읽기 모임 249번째 발표에서 소개드릴 논문은 "YOLOv4: Optimal Speed and Accuracy of Object Detection" 입니다. 논문 링크: 🤍 발표 슬라이드: 🤍 여러분들이 잘 아시는 YOLO의 4번째 버전이며 YOLOv3에서 정확도를 크게 끌어올렸습니다. 학계에서 잘된다고 알려진 여러 기법들을 적절하게 가져와서 사용하였으며, 크게 backbone, training 기법(Bag of Freebies), inference 기법(Bag of Specials)로 나눠서 여러 기법들을 적용하고 실험하여 분석하고 있습니다. 또한 모든 사람들이 쉽게 사용할 수 있게 오로지 1개의 GPU 환경에서 사용이 가능하게 설계한 점이 특징이자, 이 논문의 가장 큰 장점인 것 같습니다. Object Detection을 공부하실 때 참고하시면 많은 도움이 될 것 같은 논문입니다. 공부하시는데 도움이 되셨으면 좋겠습니다. 감사합니다.

Урок №5. YOLOv4. Обучение собственной модели

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Урок в рамках проекта BeyondRobotics 🤍 Раздел Вики: 🤍 Сайт проекта 🤍 Данный проект разработан при поддержке государственно-частного партнерства “Шеврон” и Посольства США в Казахстане.

【AI Meetup】 最強的AI物件偵測技術Yolo-v4作者親自剖析

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AI Meetup 最強的AI物件偵測技術Yolo-v4作者親自剖析 ✨2020/7/1現場+直播✨ 目前世界最強的AI物件偵測技術(Yolo-v4)來了。本中心邀請兩位作者,中央研究院資訊科學研究所廖弘源所長和王建堯博士,深入剖析這項最新的技術與未來的發展,僅此一場,切勿錯過! 44:24 開始 47:35 我們的AI計畫 廖弘源所長 1:03:10 YOLOv4的技術深入與未來方向 王建堯博士 1:51:45 QA 主辦單位:臺大人工智慧研究中心(人工智慧技術暨全幅健康照護聯合研究中心) 指導單位:科技部MOST

Python OpenCV - Aprenda a usar o Darknet Yolo V4 em 20 minutos! Detecção de objetos com Yolo V4.

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Salve galera, no video de hoje vou estar ensinando como usar o Darknet Yolo da maneira mais fácil possível. Git Darknet Yolo: 🤍 Yolo V4 cfg: 🤍 Yolo V4 weights: 🤍 Yolo Tiny V4 cfg: 🤍 Yolo Tiny V4 weights: 🤍 CoCo Names: 🤍 Insta: 🤍joao_reiis Twitter: jao_kings

How to Train YOLOv4 on a Custom Dataset in Darknet

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✅ Subscribe: 🤍 A video of how to train YOLO v4 to recognize custom objects in Google Colab in the Darknet framework. In this video we will take the following steps to train our custom detector: 1) Gather and process our dataset 2) Load dataset into Google Colab 3) Build Darknet framework in Google Colab 4) Write custom YOLO v4 training configuration 5) Train custom YOLO v4 detector 6) Use trained YOLO v4 detector for inference 7) Export YOLO v4 weights Label your images: 🤍 Corresponding training blog post: 🤍 Colab Notebook: 🤍 What is MaP? 🤍

Train a custom YOLOv4 detector online ( Free GPU )

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10.02.2021

This video shows step by step tutorial on how to train a custom YOLOv4 object detector using darknet on Google Colab. In this tutorial, I have trained a custom YOLOv4 detector for mask detection. #yolov4 #objectdetection #googlecolab #maskdetection ① ⚡⚡ My Website Blog post on this ⚡⚡ 👉🏻 🤍 ② ⚡⚡ My Colab notebook for this ⚡⚡ 👉🏻 🤍 ③ ⚡⚡ My GitHub link for custom YOLOv4 training files ⚡⚡ 👉🏻 🤍 ④ ⚡⚡ Github link for Official Darknet repository ⚡⚡ 👉🏻 🤍 ⑤ ⚡⚡ My labeled dataset ( "obj.zip" ) for YOLO ⚡⚡ 👉🏻 🤍 ⑥ ⚡⚡ Download my custom trained yolov4 weights for mask detection ⚡⚡ 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ GitHub repo with all the code and stuff: 🤍 Follow me on twitter: 🤍 Follow me on insta: 🤍 Contact me directly: ✉ 👉🏻 admin🤍techzizou.com ✉ 👉🏻 support🤍techzizou.com ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Making these videos takes a lot of time and effort, so if you like these videos and if you can, then please support the channel using any of the following: ► Buy me a coffee! ☕ 👉🏻 buymeacoffee.com/techzizou ► Support channel on Patreon! 🖖 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Thanks for watching!

사물인식 YOLO v4 실습하는 영상

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12.07.2020

대표적인 Object detection 모델 중에 하나인 YOLO v4를 가지고 실습해보겠습니다! Source code(Github): 🤍 Dependency: - Python 3 - numpy - TensorFlow 2.2+ - OpenCV 사업 및 개발문의: kairess87🤍gmail.com 빵형의 개발도상국 후원: 🤍

Real-time YOLOv4 Object Detection on Webcam in Google Colab | Images and Video

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Learn how to implement YOLOv4 Object Detection on your Webcam from within Google Colab! This tutorial uses scaled-YOLOv4, the most fast and accurate object detection system there currently is. Perform object detections in real-time on webcam images and video with high accuracy and speed. ALL WITH A FREE GPU! #yolov4 #objectdetection #cloud THE GOOGLE COLAB NOTEBOOK: 🤍 In this video I cover: 1. Setting up Colab Notebook and Enabling GPU. 2. Cloning and Building Darknet for Running YOLOv4. 3. Downloading Scaled-YOLOv4 pre-trained model file, the best object detector there is. 4. Custom Functions to run YOLOv4 with Python in Google Colab. 5. JavaScript code to access local machine's webcam for images and video. 6. Running scaled-YOLOv4 object detections on webcam images and video in real-time. Resources Github Code Repository (yolov4-webcam notebook): 🤍 Tutorial for YOLOv4 Pre-trained Model, Running on Video, Formatting Output and Detections etc.: 🤍 Train Your Own YOLOv4 Custom Object Detector in the Cloud: 🤍 Official Scaled-YOLOv4 Paper: 🤍 If you enjoyed the video, toss it a like! 👍 To Subscribe: 🤍 Thanks so much for watching! - The AI Guy

YOLOv4 Object Detection with TensorFlow, TensorFlow Lite and TensorRT Models (images, video, webcam)

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Learn how to implement a YOLOv4 Object Detector with TensorFlow 2.0, TensorFlow Lite, and TensorFlow TensorRT Models. Perform object detections on images, video and webcam with high accuracy and speed. #yolov4 #tensorflow #objectdetection This video will walk-through the steps of setting up the code, installing dependencies, converting YOLO Darknet style weights into saved TensorFlow models, and running the models. Take advantage of YOLOv4 as a TensorFlow Lite model, it's small lightweight size makes it perfect for mobile and edge devices such as a raspberry pi. Looking to harness the full powers of a GPU? Then run YOLOv4 with TensorFlow TensorRT to increase performance by up to 8x times. GET THE CODE HERE: 🤍 In this video I cover: 1. Cloning or Downloading the Code 2. Installing Required Dependencies for CPU or GPU 3. Downloading and Converting YOLOv4 Weights into a saved TensorFlow 4. Performing YOLOv4 Object Detections with TensorFlow on images, video and webcam 5. Converting TensorFlow model into a TensorFlow Lite .tflite model 6. Converting TensorFlow model into TensorFlow TensorRT model 7. Running YOLOv4 Object Detections with TensorFlow Lite -Resources Train Your Own YOLOv4 Custom Object Detector in the Cloud: 🤍 The Official YOLOv4 paper: 🤍 If you enjoyed the video, toss it a like! 👍 To Subscribe: 🤍 Thanks so much for watching! - The AI Guy

Darknet YOLOv4 Object Detection Tutorial for Windows 10 on Images, Videos, and Webcams

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23.09.2020

YOLOv4 tutorial to build Darknet YOLOv4 object detection model on Windows 10 to achieve real-time object detection on images, videos, and webcam. In this YOLOv4 tutorial, you will learn to compile Darknet YOLOv4 on your local machine with OpenCV and GPU acceleration. #TheCodingBug #YOLOv4 #Darknet - ► Time Stamps: Introduction: (0:00) Prerequisite: (0:21) Download Darknet: (03:31) Copy cuDNN and OpenCV Files: (3:55) Build Darknet using Visual Studio: (4:50) Object Detection on Images: (8:53) Object Detection on Videos: (9:48) Object Detection on Webcams: (10:34) - ► Links: Anaconda: 🤍 Visual Studio: 🤍 CUDA: 🤍 cuDNN: 🤍 YOLOv4: 🤍 - ► Commands: Images: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights Videos: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights japan.mp4 Webcams: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0 - ► My Other Tutorials: ○Build and Install OpenCV 4.5.1 With CUDA GPU Support on Windows 10: 🤍 ○ YOLOv4 On Android Using TFLite: 🤍 ○ Install TensorFlow GPU Under 90 Seconds: 🤍 ○ Install PyTorch GPU Under 90 Seconds: 🤍 ○ Custom YOLOv4 Object Detection with TensorFlow and TFLite : 🤍 ○ Darknet YOLOv4 Custom Object Detection: Part 2 (Training YOLOv4 Darknet): 🤍 ○ Darknet YOLOv4 Custom Object Detection: Part 1 (Preparing Custom Dataset): 🤍 ○ YOLOv4 Object Detection with TensorFlow, TFLite and TensorRT: 🤍 ○ Real-Time Object Detection on Webcam and Videos Using OpenCV With YOLOv3 and YOLOv4 | Windows Linux: 🤍 ○ Build and Install OpenCV 4.4.0 With CUDA (GPU) Support on Windows 10: 🤍 ○ Install TensorFlow GPU and PyTorch with CUDA on Windows 10 Anaconda | CUDA 10.1 cuDNN 7.6: 🤍 ○ Real-time Multiple Object Tracking with YOLOv4 TensorFlow and Deep Sort | Linux, Windows: 🤍 - ► Follow us on Twitter: 🤍 ► Support us on Patreon: 🤍 - DISCLAIMER: Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. There is no additional charge to you!

YoloV4 環境建置超詳細教學

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CUDA Wiki : 🤍 Darknet Github : 🤍 #YoloV4 #教學 #環境安裝

Детектируем твой пистолет на видео с камер с YOLOv4, Roboflow на Colab [RUS/ENG SUB]

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15.02.2022

В этом видео мы будем решать задачу детекции оружия с нуля при помощи нейросети YOLOv4: от поиска и сбора данных для обучения до демо на вебкамере. TG КАНАЛ - 🤍 ИНСТАГРАМ - 🤍 GOOGLE COLAB С ОБУЧЕНИЕМ YOLOV4 И ДЕМО НА ЗАПИСАННОМ ВИДЕО - 🤍 GOOGLE COLAB С ДЕМО НА ВЕБКАМЕРЕ - 🤍 ПАПКА С ПРЕДОБРАБОТАННЫМИ ДАННЫМИ, КОНФИГАМИ МОДЕЛИ И ОБУЧЕННЫМИ ВЕСАМИ - 🤍 Roboflow - 🤍 Ссылки на использованные материалы: Сравнение YOLOv4 и YOLOv5 - 🤍 a summary%2C the YOLO,state of art object detector.&text=On the other hand%2C the YOLO v5 is a new,than most of the detectors. Сравнение YOLOv4 и YOLOv5 - 🤍 1. Данные: Cкрипт для выкачивания картинок из Google Open Images - 🤍 Картинки с пистолетиками - 🤍 Датасет с инсценировкой нападения с пистолетиками с камеры наружнего наблюдения - 🤍 2. Предобработка данных Статья с аугментацией данных - 🤍 3. Обучение и демо YOLOv4 1. Официальный репозиторий - 🤍 2. theAIGuysCode - 🤍 МУЗЫКА ИЗ ТИТРОВ - ПОВОРОТ ТУДА "ГДЕ ТЫ?" - 🤍 00:00 ВВЕДЕНИЕ 03:09 СОБИРАЕМ ДАННЫЕ 08:07 ПРЕДОБРАБАТЫВАЕМ ДАННЫЕ 12:14 ОБУЧАЕМ YOLOV4 24:56 ДЕМО МОДЕЛИ НА ВЕБКАМЕРЕ 28:23 ВЫВОДЫ

Introduction to YOLOv4 object detection

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05.08.2020

In this tutorial, we'll try to understand why the release of YOLOv4 spread through the internet in just a few days. Why it's called a super-network that can, once again, change the world, same as YOLOv3 did. Most people in the field today are used to YOLOv3, which already produces excellent results. But now, YOLOv4 has improved again in terms of accuracy (average precision) and speed (FPS) - the two metrics we generally use to qualify an object detection algorithm: Text version tutorial: 🤍 Full video playlist: 🤍 GitHub code: 🤍 ✅ Support My Channel Through Patreon: 🤍 ✅ One-Time Contribution Through PayPal: 🤍

YoloV4- Install and run Yolo on NVIDIA Jetson Xavier NX with use of GPU

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01.02.2021

In this tutorial I explain step by step how to install and run YoloV4 on NVIDIA Jetson Xavier NX platform by using its 384 NVIDIA CUDA cores. For more information: 🤍

YOLOv3, YOLOv4, YOLOv5, Oh My! | OpenCV + Roboflow Webinar on the YOLO Family of Models

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07.06.2021

YOLO (You Only Look Once) is an incredibly popular computer vision model architecture. But what exactly is YOLO/ And where did it come from? Why are there so many different versions? OpenCV CEO Satya Mallick and Roboflow CEO Joseph nelson answer these questions and more in the OpenCV weekly webinar. Comprehensive YOLO Guide: 🤍 ✅ Subscribe: 🤍

Compare YOLOv4 and YOLOv4-tiny

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11.04.2021

Compare YOLOv4, YOLOv4-tiny, and two other variations. Minimal training, 4 classes, 100 annotated images, and max_batches set to 10K for the neural network. Darknet/YOLO discord: 🤍

Install Darknet framework | Object Detection using yolov4

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This video will show you how to install and setup Darknet framework on Windows (Step by Step). After setting up the darknet framework, We will test the framework with yolov4 Object Detection Algorithm. For those who are not familiar with Darknet- Darknet is a open source framework that supports Object detection. For yolov4, we will use this framework for object detection. For queries: aarohisingla1987🤍gmail.com Join this channel to get access to perks: 🤍

TRAIN A CUSTOM YOLOv4-tiny DETECTOR USING GOOGLE COLAB ( FREE GPU )

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24.02.2021

This video shows step by step tutorial on how to train a custom YOLOv4-tiny object detector using darknet on Google Colab. In this tutorial, I have trained a custom YOLOv4-tiny detector for mask detection. #yolov4-tiny #objectdetection #googlecolab ① ⚡⚡ My Website Blog post on this ⚡⚡ 👉🏻 🤍 ② ⚡⚡ My Medium article on this ⚡⚡ 👉🏻 🤍 ③ ⚡⚡ My Colab notebook for this ⚡⚡ 👉🏻 🤍 ④ ⚡⚡ My GitHub link for custom YOLOv4-tiny training files ⚡⚡ 👉🏻 🤍 ⑤ ⚡⚡ Github link for Official Darknet repository ⚡⚡ 👉🏻 🤍 ⑥ ⚡⚡ My labeled dataset ( "obj.zip" ) for YOLO ⚡⚡ 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ GitHub repo with all the code and stuff: 🤍 Follow me on twitter: 🤍 Follow me on insta: 🤍 Contact me directly: ✉ 👉🏻 admin🤍techzizou.com ✉ 👉🏻 support🤍techzizou.com ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Making these videos takes a lot of time and effort, so if you like these videos and if you can, then please support the channel using any of the following: ► Buy me a coffee! ☕ 👉🏻 buymeacoffee.com/techzizou ► Support channel on Patreon! 🖖 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Thanks for watching!

Install Yolo V4 in Windows 10

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This video is inspired by: "🤍 if you had any question you can write at the comment below –––––––––––––––––––––––––––––– Track: Effervescence — ZAYFALL [Audio Library Release] Music provided by Audio Library Plus Watch: 🤍 Free Download / Stream: 🤍 ––––––––––––––––––––––––––––––

Détection d'objet en temps réel par Deel learning (YOLOv4) avec OpenCv et Python | (code source)

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26.07.2021

Vous allez apprendre dans cette vidéo comment utiliser une intelligence artificielle plus particulièrement le deep learning pour détecter des objets dans une séquence vidéo. Nous utilisons ici le langage de programmation Python avec la librairie opencv. L'algorithme de détection d'objet que nous utilison est YOLO (You Look Only Once) qui est adapté pour les systèmes temps réel. Soutenez la chaîne! Paypal: 🤍 🔔 N'hésitez pas à vous abonner et à nous laisser votre avis en commentaires. 🤍 Navigation: (0:00) Intro (0:45) La d'objet en bref (2:22) YOLO & RCNN (3:58) détection d'objet avec OpenCV en python (13:25) Teste & Conclusion 🌐 Site officiel YOLO: 🤍 ➡️ Code source du programme: 🤍 ➡️Télécharger yolo: Cfg: 🤍 Weigths (246Mo): 🤍 Coco names: 🤍 🖋Note: Si vous n'avez pas d'expérience avec les repertoires copiez ces fichiers dans le même dossier que le programme. ➡️Liens utiles sur la détection d'objets & sources: 🤍 (Ross Girshick, et al) 🤍 (YOLOv3: An Incremental Improvement) 🤍 (YOLO object detection with OpenCV)

YOLOv4 - Advanced Tactics

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Tips and tricks for training an object detector in the YOLOv4 repo. YOLOv4 training notebook: 🤍 Original blog: 🤍

Install and run YOLOv4-Darknet on Linux(Ubuntu)

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24.08.2021

This video shows step by step tutorial on how to install and run yolov4-darknet on your local Linux system. ① ⚡⚡ Website Blog post on this ⚡⚡ 👉🏻 🤍 ⚡⚡ Medium post ⚡⚡ 👉🏻 🤍 ② ⚡⚡ Website Blog post on CUDA & cuDNN installation ⚡⚡ 👉🏻 🤍 ⚡⚡ Medium post on CUDA & cuDNN installation ⚡⚡ 👉🏻 🤍 ③ ⚡⚡ Github link for Official Darknet repository ⚡⚡ 👉🏻 🤍 ④ ⚡⚡ CUDA Toolkit download links ⚡⚡ 👉🏻 🤍 👉🏻 🤍 ⑤ ⚡⚡ Latest cuDNN version download ⚡⚡ 👉🏻 🤍 ⚡⚡ cuDNN archive download link ⚡⚡ 👉🏻 🤍 ⑥ ⚡⚡ OpenCV download link ⚡⚡ 👉🏻 🤍 ⑦ ⚡⚡ CUDA compiler Compute Capability(CC) link ⚡⚡ 👉🏻 🤍 ⑧⚡⚡Official yolov4 weights file ⚡⚡ 👉🏻 🤍 #yolov4 #objectdetection #yolov4onlinux ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ GitHub repo with all the code and stuff: 🤍 Follow me on twitter: 🤍 Follow me on insta: 🤍 Contact me directly: ✉ 👉🏻 admin🤍techzizou.com ✉ 👉🏻 support🤍techzizou.com ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Making these videos takes a lot of time and effort, so if you like these videos and if you can, then please support the channel using any of the following: ► Buy me a coffee! ☕ 👉🏻 buymeacoffee.com/techzizou ► Support channel on Patreon! 🖖 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Thanks for watching!

CPU vs GPU (Training YOLO v4). How much faster is the GPU?

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I tried training YOLO on the CPU and on the GPU to see how much faster the GPU is. I did train YOLO v4 to detect 3 objects (belt, camel and violin), starting from a dataset of a bit more than 1000 images. For this experiment I used a GPU Nvidia GTX 1660 ti and a CPU AMD Ryzen 5 2600. How faster is the Nvidia? Have you ever trained YOLO on your CPU? ➤ Full Videocourses: Object Detection: 🤍 ➤ Follow me on: Instagram: 🤍 LinkedIn: 🤍 ➤ For business inquiries: 🤍 Credits: 🤍 Background vector created by pikisuperstar - 🤍freepik.com #YOLOv4 #CPUvsGPU #training

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