Скачать или смотреть Image Recognition and Classification in one hour | Engineering project | part 2 | Quik_Code Series

I’ll walk you through testing a real-world ML project step by step — Fruit vs Flower Classification using Python, TensorFlow, Keras & OpenCV. This video will also have live demonstration of the model as well.The model which you will make willl have real time test subjects and real time testimng as well, so stay tuned till the end.This is the part 2 video of the Image classification code and I do recomend wtching that first.

Yeah! I know that I told in one hour, but come on guys! If you remove the intro and other part you will reach that much. Hope you understand.

Dataset links:
Flowers dataset : https://www.kaggle.com/datasets/alxma...
Fruits dataset : https://www.kaggle.com/datasets/molte...

(I have used only 4300 images of fruits as sample and match the data of flowers, you can use which ever you want, you can check that in part 1 video as well)

part 1:    • Image Recognition and Classification in on...  

This video is for educational puspose only and is specifically for the begineers and Engineering aspirants who are looking for start in project in their carrier or studies.

You’ll learn:
How to build a test program for the model that you have built (refer p1)
Testing a Convolutional Neural Network (CNN) from scratch
Evaluating a deep learning model
Understanding confidence level
Saving your time on minor and major engineering projects.

This project is perfect for youth engineers, students, and AI/ML beginners who want to get hands-on with deep learning basics and understand the core concepts behind image classification.

By the end of this video, you’ll not just code — you’ll understand how AI detects and to see the world! Don’t forget to LIKE , SHARE , and SUBSCRIBE for more fun and practical AI/ML projects!

This video is for educational and informational purposes only. The project and code shown are intended to help you learn the fundamentals of machine learning and artificial intelligence. While the concepts and techniques demonstrated are powerful, they are a simplified example and may not be suitable for all real-world applications.Always conduct further research and validation when developing more complex or critical systems. The datasets, models, and results presented here are for a beginner's learning environment.

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