Tutorial

Alright, class. Now that we've covered the basics of object detection theory—bounding boxes, anchor boxes, and IoU—it's time to get your hands dirty. Your first homework assignment will be a practical one: building your very own object detector. 🤖

Homework Assignment: The Everyday Object Detector The goal of this assignment is to train a simple object detection model to find a single, common object in an image. You'll be using a state-of-the-art framework and a technique called transfer learning, so don't worry, you won't be building this from scratch!

The Task: Detect a 'Soda Can' Your specific challenge is to create a model that can reliably detect a soda can. Choose any type of can you like—Coke, Pepsi, or something else—but stick to one type to keep the dataset simple.

The Steps Step 1: Data Collection & Annotation First, you need data. Your model can't learn without examples.

Collection: Take at least 50 photos of a single soda can. Vary the background, lighting, and angle. Take pictures up close, far away, with other objects in the frame, etc.

Annotation: This is the most critical part. For each photo, you'll need to draw a bounding box around every soda can present. You'll use a free, user-friendly tool like LabelImg. This tool will generate a .xml file for each image, which is a standard format for training data.

Step 2: Model Setup & Training Now that your data is ready, you'll use a pre-trained model and fine-tune it.

Model Selection: We'll use a YOLO (You Only Look Once) model. YOLO is a fantastic, popular choice for its speed and accuracy. You'll use a pre-trained version, which has already learned to recognize thousands of objects.

Transfer Learning: You'll replace the last layer of this pre-trained model and retrain it on your new, small dataset. This process, called transfer learning, is incredibly powerful because it allows you to get great results with very little data.

Training: Follow the provided tutorial on how to configure and run the training script. You'll watch your model's loss decrease over time—a sign that it's learning.

Step 3: Evaluation & Presentation Once your model is trained, you need to prove it works.

Testing: Set aside a few photos from your collection that you didn't use for training. Run your trained model on these new images to see how well it performs on unseen data.

Final Report: Prepare a brief report (a few paragraphs) summarizing your process. Include:

The number of images in your dataset.

Any challenges you faced during annotation or training.

A screenshot or two showing your model successfully detecting the soda can in a test image.

This assignment will give you a solid understanding of the entire object detection pipeline, from gathering raw data to getting a working model. Good luck, and don't hesitate to ask if you get stuck!