🔗 AI in Agriculture Course –
App 6.2: Training the Model
Welcome to another exciting tutorial in our AI in Agriculture series! 🌾 In this video, we dive deep into the process of training a YOLO V8 object detection model to detect weeds in drone footage. This step-by-step guide is perfect for anyone looking to explore machine learning applications in agriculture and understand how to create a robust weed detection system.
Key Topics Covered:
🔍 Dataset Preparation: Learn how to use and annotate datasets with RoboFlow’s tools.
🛠️ Data Augmentation: See how augmented data enhances model performance.
🖥️ Model Training with YOLO V8: Follow the process of setting up a virtual environment, configuring dependencies, and training a YOLO V8 model locally.
📈 Performance Metrics: Understand how to evaluate model performance, including precision, recall, and loss metrics.
💾 Exporting Weights: Learn how to generate and save model weights for use in applications.
🌐 Backend Integration: A sneak peek into connecting the trained model to applications via Flask APIs.
Chapters:
00:00 – Introduction to YOLO V8 Weed Detection
00:27 – Annotating and Augmenting Data
01:54 – Downloading and Preparing the Dataset
02:25 – Setting Up the Training Environment
03:19 – Training the YOLO V8 Model
04:58 – Evaluating Metrics and Exporting Weights
💡 Pro Tips:
Always use a virtual environment for each project to avoid dependency conflicts.
Monitor metrics like precision and recall to fine-tune your model effectively.
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