🎥 View full playlist here! [ Ссылка ]

🚀 In this video, we dive into the process of visualizing feature maps as we step through the first and second convolutional layers of a neural network. We explore how feature maps transform as they pass through each layer, revealing patterns and structures learned by the model. Using YOLOv5, we demonstrate how to extract and visualize these intermediate results, saving them as images and NumPy arrays. Finally, we observe how different convolutional filters help in feature extraction, focusing on shapes and patterns in the image. 🧠 🤖 💥

🔗 Colab Notebook Companion: [ Ссылка ]
(We don't use the notebook in this video. However, Colab environment setup is covered in video 8. You only need to set it up once. The notebook is then used throughout the series as a companion to the videos)

🎯 Key Highlights:
0:00 - Introduction to Feature Visualization
1:14 - Understanding the First Convolution Output
2:42 - Splitting Tensors into Channels
4:53 - Plotting and Saving Feature Maps
6:33 - Exploring Extracted Features in the Image
8:42 - Second Convolution Layer and Output
10:59 - Comparing Feature Maps Between Layers

#yolov5 #ComputerVision #ObjectDetection #AI #MachineLearning #DeepLearning #Ultralytics #YOLO

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