Learn to fine-tune a pre-trained YOLO v5 model for object detection using a custom clothing dataset in this comprehensive Python and PyTorch tutorial. Explore the fundamentals of YOLO architecture, install necessary libraries, and dive into the process of fine-tuning the model. Evaluate the results and apply the trained model to detect objects in images. Gain hands-on experience in implementing state-of-the-art object detection techniques while assessing YOLO's speed and accuracy.

Syllabus

What are we doing?
Overview of YOLO
Install required libraries
Fine-tuning the model
Evaluating the results
Detecting objects in images

Taught by

Venelin Valkov

Reviews

4.1 rating, based on 7 Class Central reviews

Start your review of Object Detection on Custom Dataset with YOLO - Fine-Tuning with PyTorch and Python Tutorial

Bharath Kumar Royal

This course provides a solid introduction to fine-tuning YOLO for custom object detection tasks using PyTorch. Venelin Valkov does an excellent job explaining the step-by-step process, making it easy to follow even for those who are relatively new to object detection or deep learning. The tutorial covers essential topics like dataset preparation, training, and evaluation, all while being practical and hands-on. However, I wish the course included more details about handling edge cases, such as imbalanced datasets or improving accuracy when results are suboptimal. Overall, it’s a great resource for anyone looking to get started with YOLO and PyTorch for custom datasets.
Hari Sudhan

YOLOv5 (You Only Look Once version 5) is a cutting-edge computer vision model and course that has garnered significant attention in the field of deep learning and object detection. It represents a significant advancement in real-time object detecti…

YOLOv5 (You Only Look Once version 5) is a cutting-edge computer vision model and course that has garnered significant attention in the field of deep learning and object detection. It represents a significant advancement in real-time object detection with improved accuracy and speed.

The YOLOv5 course is an invaluable resource for AI and machine learning enthusiasts. It covers the entire YOLOv5 architecture, from the basics to advanced techniques. Students can expect hands-on experience with training and deploying YOLOv5 models for various applications, including object detection in images and videos.

With its comprehensive content and practical approach, the YOLOv5 course equips learners with the skills needed to harness the power of this state-of-the-art model for real-world projects. Whether you're a beginner or an experienced practitioner, this course offers a deep dive into YOLOv5, making it an essential resource in the world of computer vision and object detection.
Akash Kumar

It was a fine tutorial for beginning. Although this can not help one to start any project or so. You have to dwell more in deep for that.
Pritesh Lungare

This tutorial on custom object detection with YOLO, fine-tuned using PyTorch and Python, is exceptional! Clear explanations, step-by-step guidance, and practical examples make it a valuable resource for anyone diving into the world of computer vision. Highly recommended!
Sirine BELHAJ

The tutorial on Object Detection with YOLO (v5) fine-tuning using PyTorch and Python is well-structured and informative. The step-by-step guide is clear, making it accessible even for those with limited experience in the field. The use of PyTorch enhances readability, and the inclusion of a custom dataset adds practical value. However, more detailed explanations in certain sections could benefit beginners. Overall, a valuable resource for anyone looking to delve into custom object detection with YOLO (v5).
Nadim Kamal

This was amazing course and easy to understand. With the help of this course I have come to know about the basis of object detection and it was pretty easy. Now I can visualize easily with the completion of this course & I am grateful to the courses instructor. It was really helpful. How can I get certified?
Muhammad Abbas

The course was best for me. If gives me a new path in detection of images. I can detect many classes easily. it gives best accuracy and also gives fast work as well. It is best then CNN and other model in case of speed and accuracy

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