Learn Data Science using Python

  Greetings, computer vision practitioners👮! I hope you're all doing well in your projects. Recently, I received a new requirement from a client regarding an object detection solution that had been successfully deployed as a REST API. The solution was seamlessly integrated into both web and mobile applications. However, the client encountered network reliability issues, particularly in rural areas where poor connectivity occasionally caused the solution to fail on mobile devices💔. As we know, network disruptions are a common challenge, but they become critical when they impact business operations. My client sought a robust solution to address this issue, ensuring uninterrupted functionality regardless of network conditions. In this blog post, we will explore how I tackled this challenge and implemented an effective solution💓. The Solution: Enabling Offline Object Detection for Mobile Devices Given that network connectivity issues are beyond our control, the most effective soluti...

Another post starts with you beautiful people 😊 Thank you all for messaging me your doubts about Darknet's YOLOv4. I am very happy to see in a very short amount of time my lovely aspiring data scientists have learned a state of the art object detection and recognition technique. If you are new to my blog and to computer vision then please check my following blog posts one by one- Setup Darknet's YOLOv4 Train custom dataset with YOLOv4 Create production-ready API of YOLOv4 model Create a web app for your YOLOv4 model Since now we have learned to use YOLOv4 built on Darknet's framework. In this post, I am going to share with you how can you use your trained YOLOv4 model with another awesome computer vision and machine learning software library-  OpenCV  and of course with Python 🐍. Yes, the Python wrapper of OpenCV library has just released it's latest version with support of YOLOv4 which you can install in your system using below command- pip install opencv-pyt...

Another post starts with you beautiful people! Today an interesting deep learning challenge is finished in  HackerEarth  and I got 91.17026 mAP score in the leader board. One drawback I see in HackerEarth is due to small dataset many participants manually prepare the submission files and show 100% score in the leader board. Many aspiring data scientists see this and become nervous. Even with getting score 75+, they become demotivated and leave their experiments in between the challenge. Also the winning approach is not disclosed after the challenge. With this post I will try to motivate my all aspiring data scientists and I will share my solution so that in their next challenge they can easily get 85+ score or even 92+ score :) Problem statement An event management company organized an evening of Indian classical dance performances to celebrate the rich, eloquent, and elegant art of dance. After the event, the company plans to create a micro site to promote and raise aw...

Another post starts with you beautiful people! Thank you all for your overwhelming response in my last two posts about the YOLOv4. It is quite clear that my beloved aspiring data scientists are very much curious to learn state of the art computer vision technique but they were not able to achieve that due to the lack of proper guidance. Now they have learnt exact steps to use a state of the art object detection and recognition technique from my last two posts. If you are new to my blog and want to use YOLOv4 in your project then please follow below two links- How to install and compile Darknet code with GPU? How to train your custom data with YOLOv4? In my  last post we have trained our custom dataset to identify eight types of Indian classical dance forms. After the model training we have got the YOLOv4 specific weights file as 'yolo-obj_final.weights'. This YOLOv4 specific weight file cannot be used directly to either with OpenCV or with TensorFlow currently becau...

Another post starts with you beautiful people! Thank you all who had followed my last post about  install and compile YOLOv4 in Windows10   and could able to successfully set up the Darknet in their machines. As I promised in last post and you asked for, in this post I am going to share you the steps required for training a custom object with YOLOv4. If you are seeing my blog first time, I recommend you to first follow my  last post  and then proceed further. For this exercise I have choosen a dataset of eight Indian Classical Dance forms- Manipuri from Manipur Bharatanatyam from Tamil Nadu Odissi from Orissa Kathakali from Kerala Kathak from Uttar Pradesh Sattriya from Assam Kuchipudi from Andhra Pradesh Mohiniyattam from Kerala You can download the dataset from this hackethon link . After downloading the dataset , you need to create 8 folders with class name and copy respective images from train folder to there. For this work I have writt...