Coding for object recognition involves building a model that detects and classifies objects in an image. Start by choosing a framework like TensorFlow or PyTorch and a pre-trained model such as YOLO, Faster R-CNN, or SSD for faster development.
Preprocess the input images by resizing and normalizing them to match the model’s requirements. Use the pre-trained model for inference by feeding an image and obtaining bounding boxes, class labels, and confidence scores. Fine-tune the model on a custom dataset for specific tasks if needed.
For custom implementation, design a convolutional neural network (CNN) architecture, define loss functions for classification and localization, and train it on annotated datasets like COCO or Pascal VOC.