To create an object recognition system, start by defining the application scope, such as detecting objects in images or real-time video. Collect and annotate a dataset with bounding boxes or labels for the objects of interest.
Use a deep learning framework like TensorFlow or PyTorch to train a convolutional neural network (CNN). Pre-trained models like YOLO, SSD, or Faster R-CNN can save time and resources, requiring only fine-tuning on your dataset.
Evaluate the model’s accuracy using metrics like mean average precision (mAP). After training, deploy the system on a suitable platform, such as a web app or edge device, ensuring it meets real-time requirements if needed.