Building a Simple Object Detection Model

Building a simple object detection model involves several key steps, combining data preparation, model selection, training, and evaluation. First, you’ll need to gather and label a dataset containing images with various objects of interest. Popular datasets include COCO or custom datasets using tools like LabelImg. Next, you’ll choose a suitable model architecture, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), known for their efficiency and accuracy. After setting up your environment with necessary libraries like TensorFlow or PyTorch, you’ll preprocess the data, including resizing images and normalizing pixel values. The model is then trained on your labeled dataset, where it learns to identify and localize objects by adjusting its internal parameters. Once training is complete, you’ll evaluate the model using metrics such as mean Average Precision (mAP) to gauge its performance. Finally, you can visualize the results by plotting bounding boxes around detected objects in test images, refining the model as needed based on its accuracy and precision. This project not only reinforces foundational concepts in AI/ML but also provides hands-on experience with practical applications in computer vision.