Learn Data Science using Python

Another post starts with you beautiful people! Thank you all for showing so much interests in my last posts about object detection and recognition using YOLOv4. I was very happy to see many aspiring data scientists have learnt from my past three posts about using YOLOv4. Today I am going to share you all a new skill to learn. Most of you have seen my post about  deploying and consuming ML models as Flask API   where we have learnt to deploy and consume a keras model with Flask API  . In this post you are going to learn a new framework-  FastAPI to deploy your model as Rest API. After completing this post you will have a new industry standard skill. What is FastAPI? FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is easy to learn, fast to code and ready for production . Yes, you heard it right! Flask is not meant to be used in production but with FastAPI you can use your model in production wi

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 written a simple Python