Lilly Tech Systems
Computer Vision Interview Prep
Prepare for computer vision interviews at top tech companies. From CNN fundamentals and image classification to object detection, segmentation, vision transformers, and production deployment — real interview questions with detailed model answers and runnable code examples that reflect what hiring teams actually ask in 2024–2026.
Your Learning Path
Start with the CV interview landscape, master core vision tasks, then tackle advanced topics and practical deployment challenges.
1. CV Interview Overview
Computer vision role types, what to expect in CV interviews at top companies, preparation tips, and how to structure your study plan for 2024–2026.
2. Image Classification Questions
12 Q&A covering CNN architectures (ResNet, EfficientNet, VGG), transfer learning, data augmentation, evaluation metrics, and code examples.
3. Object Detection Questions
12 Q&A on YOLO, SSD, Faster R-CNN, anchor boxes, non-maximum suppression, mAP, IoU, feature pyramid networks, and code examples.
4. Segmentation Questions
10 Q&A on semantic vs instance vs panoptic segmentation, U-Net, Mask R-CNN, loss functions, evaluation metrics, and medical imaging applications.
5. Advanced CV Topics
10 Q&A on GANs for CV, self-supervised learning, vision transformers (ViT, DINOv2, SAM), 3D vision, video understanding, and multimodal models.
6. Practical CV Challenges
10 Q&A on model deployment, edge inference (TensorRT, ONNX), data labeling strategies, model optimization, real-time processing, and MLOps for CV.
7. Practice Questions & Tips
Rapid-fire questions, coding challenges, FAQ accordion, and strategic tips for acing your computer vision interview from preparation to offer.
What You'll Learn
By the end of this course, you will be able to:
Answer Core CV Questions
Confidently explain CNN architectures, pooling operations, batch normalization, skip connections, and the evolution from AlexNet to EfficientNet with technical depth.
Tackle Detection & Segmentation
Discuss anchor-based vs anchor-free detectors, NMS, FPN, U-Net skip connections, and instance vs panoptic segmentation like a practitioner.
Master Advanced Topics
Handle questions on vision transformers, self-supervised learning (MAE, DINO), GANs, 3D vision, and video understanding with confidence.
Deploy CV Models
Discuss real-world deployment: model quantization, ONNX export, TensorRT optimization, edge inference, data pipelines, and production monitoring.