ML Interview Questions

Describe the evolution of object detection techniques from R-CNN to YOLO, focusing on the improvements each method introduced. Discuss the impact these advances have had on both accuracy and speed in practical applications.

Explain the evolution of object detection architectures in computer vision. Compare and contrast two-stage detectors like the R-CNN family with one-stage detectors such as YOLO and SSD. Assess their architectures, training methodologies, performance metrics like mAP and inference speed, and practical trade-offs. Additionally, discuss the application of transformers in modern object detection approaches.

Discuss the various types of image segmentation techniques such as semantic, instance, and panoptic segmentation. How do these differ in their approach and application? Compare and contrast key architectures like U-Net, Mask R-CNN, and Panoptic FPN in terms of their effectiveness, complexity, and real-world deployment.

Describe how a Convolutional Neural Network (CNN) is utilized in modern face recognition systems. What are the key stages from image preprocessing to feature extraction and finally recognition? Discuss the challenges encountered in implementation and the metrics used to evaluate face recognition models.