Question 1

Computer Vision vs NLP salary, why CV specialists earn more?

Accepted Answer

CV roles in autonomous driving, robotics, medical imaging command $30-50k premium over general ML. Data scarcity (labeling images = expensive + slow) elevates specialist pay. NLP commoditized faster (LLMs, transformers). 2026: multimodal engineers bridge both, earning $160-220k top-of-band.

Question 2

Foundation models like SAM/CLIP, do I still need CNN expertise?

Accepted Answer

Yes and no. SAM (Segment Anything) + CLIP enable zero-shot segmentation/classification without fine-tuning. But production work still requires understanding backbone architectures, optimization, edge deployment. 80% of jobs use pre-trained models + fine-tuning, not training from scratch. Learn YOLO, Detectron2 first.

Question 3

How do I deploy CV models to edge devices (phone/robot)?

Accepted Answer

Convert PyTorch → ONNX → TensorRT (NVIDIA GPU), TFLite (mobile), or CoreML (iOS). Quantization (int8) + pruning cut size 10x-20x. NVIDIA TAO (no-code) compresses fast. Budget 2-3 months for edge deployment. Inference speed on RPi/iPhone = hard constraint; start with ONNX export early.

Question 4

Dataset bottleneck, 10k labeled images vs 1M unlabeled. What's viable?

Accepted Answer

Transfer learning salvages small datasets. ResNet50 pre-trained on ImageNet achieves 85% acc with 5k images in 1-2 days. For custom objects: Roboflow auto-augmentation. If <5k images: semi-supervised learning (pseudolabeling) + synthetic data. Data > Model, invest here first.

Question 5

What's the difference: classification vs detection vs segmentation?

Accepted Answer

Classification: 1 label per image ('cat' or 'dog'). Detection: bounding boxes + labels (YOLO finds multiple objects). Segmentation: pixel-level masks (where exactly is each object). Instance segmentation = detection + segmentation. Panoptic = stuff (sky) + things (objects). Job paths differ: classification starter, detection L2, segmentation/panoptic L3+.

Question 6

Vision Transformers vs CNNs in 2026, which should I learn?

Accepted Answer

ViTs (DeiT, Swin) beat CNNs on ImageNet, but need more data (1M+). CNNs still rule production (YOLO, ResNet) due to efficiency, interpretability, mature tooling. Hybrid: use transformer backbone in detection head. Learn both. Future: 90% transformer-based within 3 years.

Question 7

How do I get from 'Hello CV' to production-ready system?

Accepted Answer

Month 1-3: CNNs (ResNet, data augmentation), YOLO basics, 1 Kaggle competition. Month 4-6: fine-tune on your domain data, quantization, edge export. Month 7-9: monitoring (drift detection), retraining pipelines, A/B testing models. Production = 40% model, 60% data + monitoring + iteration.

प्रदेश	ज्युनियर	मध्यम	सीनियर
USA	$110k	$160k	$240k
UK	£65k	£95k	£140k
EU	€70k	€100k	€150k
CANADA	C$115k	C$165k	C$250k

Computer Vision (CV)

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