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Train a Custom YOLOv5 Model on Google Colab and Detect Objects In Real Time on Windows | Part 3
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🚀Hey everyone and welcome to the final part of the YOLOv5 series!
In this video (Part 3) we'll learn about training a custom YOLOv5 PyTorch model on Google Colab, and using Weights & Biases to understand the training process.
We'll start off by discussing the differences between pre-trained and custom-trained models, and how we can train using the custom dataset that we collected in part 2. Then, we'll use Weights & Biases to determine which of the models we trained is the best one, and deploy that model locally on Windows to perform real-time detection using a custom-trained model.
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Links
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You can watch the whole YOLOv5 Series here:
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Follow Ivan:
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⏳ Timestamps ⏳
00:00 Intro
00:50 How to think about training a YOLOv5 to detect new classes
03:28 Training a custom YOLOv5 model on Google Colab
08:23 How Weights & Biases helps you understand the training process
12:12 Training a large YOLOv5 model
16:18 Deploying a custom-trained model on Windows
18:28 Outro
In this video (Part 3) we'll learn about training a custom YOLOv5 PyTorch model on Google Colab, and using Weights & Biases to understand the training process.
We'll start off by discussing the differences between pre-trained and custom-trained models, and how we can train using the custom dataset that we collected in part 2. Then, we'll use Weights & Biases to determine which of the models we trained is the best one, and deploy that model locally on Windows to perform real-time detection using a custom-trained model.
---
Links
---
You can watch the whole YOLOv5 Series here:
---
Follow Ivan:
---
⏳ Timestamps ⏳
00:00 Intro
00:50 How to think about training a YOLOv5 to detect new classes
03:28 Training a custom YOLOv5 model on Google Colab
08:23 How Weights & Biases helps you understand the training process
12:12 Training a large YOLOv5 model
16:18 Deploying a custom-trained model on Windows
18:28 Outro
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