📌 Objective: Explain the project and capture images for training the model.

• Brief introduction to CNNs, IoT, and computer vision.
• Explanation of the training and inference process.
• Setting up the Raspberry Pi camera.
• Capturing images, organized by category (at least 20 per class).
• Uploading images to a PC or Google Drive for training.

💡 By the end of this session, participants will have a dataset ready for preprocessing.

In this step, we will manually capture images of different types of waste using a smartphone or digital camera. Each type of waste (paper, plastic, glass, organic) must be stored in separate folders. Later, these images will be used to train our AI model. It is important to take at least 20 images per category from different angles and lighting conditions.

📝 Instructions:

1. Use a phone or camera to take at least 20 photos per category (paper, plastic, glass, organic).
2. Create the following folder structure:

dataset/
│── paper/
│   ├── image1.jpg
│   ├── image2.jpg
│── plastic/
│   ├── image1.jpg
│   ├── image2.jpg
│── glass/
│   ├── image1.jpg
│   ├── image2.jpg
│── organic/
│   ├── image1.jpg
│   ├── image2.jpg

3. Transfer these images to your computer or Google Drive for processing in Jupyter Notebook.