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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
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: 🤍 📸 Dhaval's Personal Instagram: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin: 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.
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 🤍
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
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.
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
This video titled "YOLOv4 Object Detection Crash Course | YOLO v4 how it works and how to build it | Introduction" gives the introduction of Yolo V4 object detection framework i.e. what exactly it is, a brief explanation of various components of its architecture, different use cases of it as well as a demo of it at the end of the video. YOLO stands for "You Look Only Once". It is a state of art real-time object detection framework. It uses a Convolutional Neural network to detect objects in the image or video. YOLO V4 version is very fast, more accurate and can process any video 65 fps. It is a very good choice when you need real-time detection, without loss of too much accuracy. YOLO framework is really good in terms of detecting multiple objects in an image or video hence not only good at predicting different classes in the image but also their actual location. Yolo v4 Paper : 🤍 The AI University Website: 🤍theaiuniversity.com Get the "The AI University" Android App on Google Playstore Join this channel to get access to perks: 🤍 FOLLOW ME ON: Twitter: 🤍 Facebook : 🤍 Instagram: 🤍 Telegram: 🤍 Tool for Keyword Research, Channel Health, Thumbnail Generation for your channel : 🤍 ▶ GITHUB REPO : 🤍 About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. *Other AI, ML, Deep Learning, Augmented Reality related Video Series* Deploy Machine Learning Models as Web App using Flask & Docker on Azure Cloud - 🤍 Machine Learning Data Pre-processing & Data Wrangling using Python - 🤍 Machine Learning & Deep Learning Project - 🤍 Machine Learning Projects in HINDI - 🤍 Deep Learning Neural Network Tutorials - 🤍 Machine Learning & Deep Learning Bootcamp Series - 🤍 Machine Learning using Spark MLLib - 🤍 Augmented Reality Free Tutorial - 🤍 Data Engineering Full Hands-on Course - 🤍 Hadoop, Machine & Deep Learning on Azure Cloud Tutorial Series - 🤍 Natural Language Processing - 🤍 Develop Dashboard for Business Intelligence & Data Science(Plotly Dash Tutorial Series) - 🤍 Data Science Tip and Tricks and Career Advice - 🤍 Machine Learning, Deep Learning Maths(Matrix & Vector Operations) - 🤍 DISCLAIMER: This video and description may contain affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. #YOLOv4 #ObjectDetection #LabelImages
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: 🤍
* 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: 🤍
Basic Intuition of YOLO model for object detection Donate me: 🤍 #objectDetection #yolov5
Make YOLOV4 TFLite Object Detection Mobile app for Android using YOLOv4 Tiny, YOLOV4, and custom trained YOLOv4 TFLite models. TFLite YOLOv4 Tiny model along with object detection android app code can be obtained from the GitHub repository linked below while we converted YOLOV4 and custom YOLOV4 to TFLite using one of our other tutorials linked below. *APK files for all three TFlite models are available for our Patreon supporters* Links: 🤍 Want to discuss more? ►Join my discord: 🤍 #TheCodingBug #tflite #yolov4 - ► My Other Tutorials: ○Build and Install OpenCV 4.5.1 With CUDA GPU Support on Windows 10: 🤍 ○ Custom YOLOv4 Object Detection with TensorFlow and TFLite : 🤍 ○ Install TensorFlow GPU Under 90 Seconds: 🤍 ○ Install PyTorch GPU Under 90 Seconds: 🤍 ○ 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: 🤍 ○ Darknet YOLOv4 Object Detection for Windows 10 on Images, Videos, and Webcams: 🤍 ○ 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!
YOLO v4 object detection Achieved around 36fps on RTX2060 Super Darknet repo - 🤍
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!
In this video, we will see how to create docker yolo v4 image from github and detect objects via webcam. Please email dotslashrun.sh🤍gmail.com, if you need training on docker.
CUDA Wiki : 🤍 Darknet Github : 🤍 #YoloV4 #教學 #環境安裝
Distance estimation using yolov4 object detector OpenCV python, here we are using a single camera to estimate distance, source code: 🤍 GitHub: 🤍 Instagram: 🤍 Facebook: 🤍 Song: Vellz - It Will Come To You (ft. Sergi Yaro) Music Released by FreeMusicWave. Link: 🤍 Free Download/Stream: 🤍 ignore these: #distanceestimation #YoloV4 #YoloV4objectDetector yolo distance estimation yolo distance estimation github #opencv #opencv-python #python #python-projects #computer-Vision distance estimation yolov4,yolo distance estimation github,distance estimation yolo,distance estimation opencv-python,object detection and distance estimation,distance estimation using single camera,yolo distance estimation,Distance estimation from monocular rgb camera,opencv-projects,Object detection,stereo camera
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 😊 - 🤍 Learn how you can develop your own Social distancing monitoring app using YOLOv4. ⭐6-in-1 AI Mega Course - 🤍 ►YOLOv4 Course + Github - 🤍 ►Ultimate AI-CV Webinar - 🤍 In this little demo that you are seeing right now is an app that is demonstrating how YOLOv4 can be used for social distancing. So, in this tutorial, I am going to show you how you can implement your own social distancing app using YOLOv4 which can be used to fight COVID-19 a.k.a. the Corona Virus. So how the app works is that after we perform detection with YOLOv4, we calculate the Euclidean distance between all the detected boxes and filter out or flag the people that are closest to each other indicating that they are at risk of COVID-19. 0:00 Introduction 2:30 Social Distancing Monitoring App 3:21 Coding 3:35 Euclidean Distance Code 4:15 cvDrawboxes Code 4:27 3.1 Person Filter 7:07 3.2 Social Distance Criteria 9:57 3.3 Risk Indication 12:23 Running the Demo ►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. If you want to implement the latest YOLOv4 on social distancing, then check out this video tutorial.
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
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 ⚡⚡ 👉🏻 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ If you like these videos, please support the channel on YouTube through Thanks or YouTube Membership! Thanks 🖖 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 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 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Thanks for watching!
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
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!
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
Compare YOLOv4-tiny and YOLOv7-tiny using a custom dataset. Please watch my other tutorial videos if you are looking to create neural networks like this. For example, start here: 🤍 Darknet/YOLO discord server: 🤍
#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.
This video titled "Train CUSTOM Object Detection Model using YOLOv4 | CUSTOM Object Detection on YOLOv4 Darknet" explains the detailed steps to train a custom object detection model on our own custom dataset and that we downloaded from the open image dataset tool in the earlier videos. The model would be trained on Google Colab GPU Acceleration Mode. One of the other reason for using Google Colab is that a lot of dependencies gets fulfilled automatically when we try to train our model here. YOLO uses Convolutional Neural Network to train these models so we need to use GPU machines for that purpose. The AI University Website: 🤍theaiuniversity.com Get the "The AI University" Android App on Google Playstore Join this channel to get access to perks: 🤍 FOLLOW ME ON: Twitter: 🤍 Facebook : 🤍 Instagram: 🤍 Telegram: 🤍 Tool for Keyword Research, Channel Health, Thumbnail Generation for your channel : 🤍 ▶ GITHUB REPO : 🤍 About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. *Other AI, ML, Deep Learning, Augmented Reality related Video Series* Deploy Machine Learning Models as Web App using Flask & Docker on Azure Cloud - 🤍 Machine Learning Data Pre-processing & Data Wrangling using Python - 🤍 Machine Learning & Deep Learning Project - 🤍 Machine Learning Projects in HINDI - 🤍 Deep Learning Neural Network Tutorials - 🤍 Machine Learning & Deep Learning Bootcamp Series - 🤍 Machine Learning using Spark MLLib - 🤍 Augmented Reality Free Tutorial - 🤍 Data Engineering Full Hands-on Course - 🤍 Hadoop, Machine & Deep Learning on Azure Cloud Tutorial Series - 🤍 Natural Language Processing - 🤍 Develop Dashboard for Business Intelligence & Data Science(Plotly Dash Tutorial Series) - 🤍 Data Science Tip and Tricks and Career Advice - 🤍 Machine Learning, Deep Learning Maths(Matrix & Vector Operations) - 🤍 DISCLAIMER: This video and description may contain affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. #CustomObjectDetection #GoogleColab #YOLOv4Darknet
In this tutorial, we will see how to use Jetson Xavier NX with YOLO v4 and darknet. All operations for Download, Install, and Run Darknet with YOLOv4 are folded step by step and with pictures. You will have no problem finding the Nvidia JetPack or identifying objects in real-time or in videos. Blog: 🤍 ➤ Full Video courses: Object Detection: 🤍 ➤ Follow me on: Instagram: 🤍 LinkedIn: 🤍 ➤ For business inquiries: 🤍 #JetsonXavierNX #YOLOV4 #DeepLearning
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: 🤍
This video shows step by step tutorial on how to train a custom YOLOv4 object detector using darknet on your local Linux machine. In this tutorial, I have trained a custom YOLOv4 and YOLOv4-tiny detector for mask detection. ① ⚡⚡ My Website Blog post on this ⚡⚡ 👉🏻 🤍 👉🏻 🤍 ⚡⚡ Medium post on this ⚡⚡ 👉🏻 🤍 👉🏻 🤍 ② ⚡⚡ My GitHub link for Linux custom YOLOv4 training files ⚡⚡ 👉🏻 🤍 ③ ⚡⚡ My GitHub link for Linux custom YOLOv4-tiny training files ⚡⚡ 👉🏻 🤍 ④ ⚡⚡ Github link for Official Darknet repository ⚡⚡ 👉🏻 🤍 ⑤ ⚡⚡ My labeled dataset ( "obj.zip" ) for YOLO ⚡⚡ 👉🏻 🤍 ⑥ ⚡⚡ My custom trained yolov4 weights files for mask detection ⚡⚡ 👉🏻 🤍 ⑦ ⚡⚡ Pre-trained yolov4 weights file ⚡⚡ 👉🏻 🤍 ⑧ ⚡⚡ Pre-trained yolov4-tiny 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!
This tutorial shows you how to train a YOLOv4 object detection model on your own dataset using free tools (Roboflow, Google Colab). The YOLOv4 implementation is in PyTorch, but the model can be exported with ONNX to TensorFlow. Create a free Roboflow account: 🤍 Access to the Google Colab notebook: 🤍 Convert to ONNX and TensorFlow: 🤍
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!
هذه المحاضرة هي جزء من سلسلة محاضرات التعلم العميق Deep Learning يمكنك مشاهدة جميع الحلقات هنا 🤍 و يمكنك متابعتنا علي الصفحة الخاصة بنا علي الفيس بوك 🤍 كما يمكنك الانضمام للمجموعة الخاصة بنا هنا 🤍
Templete Service Repo: 🤍 Yolo_Object_Detection_Service Repo: 🤍 Yolo-v4 Original Repo: 🤍 In this tutorial video one can learn about: 1. How to pull code to your google colab notebook from github repositories. 2. How to setup environment in google colab. 3. How to run inference on Object Detection model from YOLO. #fastapi #ml #yolo #yolov4 #objectdetection #webservice #machinelearning #kubernetes #poetry #docker #helm #googlecolab
Training custom object detector using YOLOv4 Darknet has its benefits. Darknet based custom object detection model is faster than TensorFlow based object detector. In this video, we are going to learn how to prepare a dataset for training YOLOv4 custom object detection model using Darknet on Windows 10. This tutorial is split into two parts. In the first part, we will create a custom object detection dataset by downloading images from Google with an automatic script. These images will then be annotated for custom object detection using YOLOv4. In the second part, we will train darknet yolov4 and run a custom object detector on images and videos. To train YOLOv4 to detect custom object, I chose "John Wick" as an example. You can choose any object of your preference. Labeled Dataset is available for our Patreon supporters. 🤍 Link to Part 2: 🤍 Links to repositories used in the video: 🤍 🤍 🤍 🤍 #TheCodingBug #YOLOV4 #Darknet - ► Time Stamps: Introduction: (0:00) Prerequisite: (0:28) Download images from Google: (1:03) Label the data: (3:38) - ► 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): 🤍 ○ YOLOv4 Object Detection with TensorFlow, TFLite and TensorRT: 🤍 ○ Darknet YOLOv4 Object Detection for Windows 10 on Images, Videos, and Webcams: 🤍 ○ 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!
In this video, I've explained about the YOLO (You Only Look Once) algorithm which is used in object detection. Object detection is a critical capability of autonomous vehicle technology. It’s an area of computer vision that’s exploding and working so much better than just a few years ago. YOLO is a clever convolutional neural network (CNN) for doing object detection in real-time. The algorithm applies a single neural network to the full image, and then divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. YOLO is popular because it achieves high accuracy while also being able to run in real-time. The algorithm “only looks once” at the image in the sense that it requires only one forward propagation pass through the neural network to make predictions. After non-max suppression (which makes sure the object detection algorithm only detects each object once), it then outputs recognized objects together with the bounding boxes. With YOLO, a single CNN simultaneously predicts multiple bounding boxes and class probabilities for those boxes. YOLO trains on full images and directly optimizes detection performance. This model has a number of benefits over other object detection methods. Some research papers on YOLO for better understanding of the algorithm: 🤍 🤍 🤍 GitHub: 🤍 LinkedIn: 🤍 #yolo #ObjectDetection #CNN #Python
This time, let's make a faster Aimbot with YOLO(Darknet). And there is also a guide for building OpenCV CUDA libraries. OpenCV CUDA Guide: 🤍 Code: 🤍
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 😊 - 🤍 Get Ready for YOLOv4 using OpenCV Python Computer Vision PRO Course, the course that you have been waiting for... This is the AI Object detection course with YOLOv4 - [ENROLLEMENTS NOW OPEN -] - Click the link below to sign up. ⭐6-in-1 AI Mega Course - 🤍 ⭐YOLOv4 Course - 🤍 In a world plagued by COVID-19 and whilst going out is forbidden. In the midst of all this gloom, through the shadows, there shines a glimpse of hope. Will you be the one to rise above this calamity, using only the tools of Artificial Intelligence and Computer vision to combat the spread of the Corona Virus. Will you harness the power of Computer Vision, Python and YOLOv4 Object Detection to make a change and fight for freedom! - 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 #YOLOv5 #artificialintelligence #computervision #objectdetection - We teach YOLO v2, YOLO v3 and YOLOv4. If you want to implement the latest YOLOv4 on social distancing, then check out this video tutorial.
This video present one of the fastest object detection algorithms for videos that can be used for real time applications. The algorithm is made easy for beginners. This is part 1, and part 2 will also be uploaded soon.
website : 🤍 instagram : 🤍 email : renalfarhan🤍rnfproject.id Dalam perkembangan teknologi yang pesat, perhatian yang signifikan diberikan pada makanan yang kita konsumsi. Salah satu faktor yang paling menuntut biaya dalam industri pertanian adalah tenaga kerja terampil. Industri bergerak menuju otomatisasi untuk mengurangi biaya kerja dan meningkatkan kualitas. Pemanenan robot dapat memberikan solusi potensial untuk banyak masalah seperti yang dihadapi industri. Ada banyak tugas menantang yang harus diselesaikan oleh teknologi yang akan datang, salah satunya adalah sistem deteksi buah yang akurat. Teknologi yang berbeda telah digunakan untuk pengenalan buah menggunakan teknologi visi komputer yang muncul. Proyek khusus membahas membangun model yang kuat untuk deteksi buah. Ada banyak kasus penggunaan lanjutan untuk ini, dan beberapa di antaranya adalah: Anda bekerja di gudang di mana jutaan buah masuk setiap hari, dan jika Anda mencoba memisahkan dan mengemas setiap kotak buah secara manual, itu akan membutuhkan banyak tenaga kerja. Jadi, Anda dapat membangun sistem otomatis yang dapat mendeteksi buah dan memisahkannya untuk pengemasan. Anda adalah pemilik anggrek besar. Jika ingin memanen buah secara manual, maka akan membutuhkan banyak tenaga kerja. Anda dapat membuat robot atau truk swakemudi yang dapat mendeteksi buah di pohon tertentu dan memanennya untuk Anda keyword : yolo v4 , deep learning, fruit detection, machine learning, real time
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![[FULL PROGRAM] Real Time Fruits Detection Using Yolo V4](https://i.ytimg.com/vi/8HiNSogXpeE/mqdefault.jpg "[FULL PROGRAM] Real Time Fruits Detection Using Yolo V4")