Training a YOLO model on custom Dataset and deploying it in real-time.

Описание: Welcome to my comprehensive guide on building a custom YOLO (You Only Look Once) object detection model tailored to your specific dataset! Whether you're in computer vision research or developing practical applications, this tutorial will walk you through the entire process.

In this video, I'll cover:

Data Collection and Annotation: Learn to gather a diverse dataset and annotate images using popular tools like Cvat. Annotation is key for training the model to detect objects accurately.

Dataset Preparation: Understand the importance of splitting your dataset into training, validation, and testing sets, using roboflow. Proper distribution and preprocessing ensure your model's robustness.

Model Training: Use a pre-trained YOLO model (Yolov5s) and fine-tune it on your custom dataset. We'll walk you through using PyTorch for training and implementing data augmentation techniques for better generalization.

I actually used Rock, Paper, Scissors in this model but you can use whatever
dataset you like

Timestamps:
0:00 - Introduction
1:10 - Installing our dependencies
2:35 - Loading the model
3:00 - Detect from an image
8:50 - Detecting from a video or real-time
9:50 - Collecting the dataset
13:15 - Labeling/annotating our images
18:45 - Data Augmentation
23:50 - Training the model
28:50 - Testing in real-time

the GitHub repo: https://github.com/Daiki2003/Yolov5

Ultralytics Yolov5: https://github.com/ultralytics/yolov5

CVAT: https://www.cvat.ai/

Roboflow: https://roboflow.com/

#coding #python #deeplearning #machinelearning #ai #object_detection #yolo #yolov5