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全部技能

Computer Vision (CV)

Teach computers to see: image classification, object detection, segmentation

⬢ 層級 2技術
+$40k-
薪資影響
12 個月
學習時長
困難
難度
12
職業
一覽

Computer Vision enables machines to understand images/video through CNNs, transformers, and generative models. Applications: autonomous vehicles, medical imaging, AR/VR, quality control. Career path: Practitioner (image classification, $95-130k) → Specialist (object detection, segmentation, $130-180k) → Expert (3D vision, multimodal models, $180-260k). Requires solid ML + Python foundation. 2026 hot: multimodal LLMs + foundation models (SAM, CLIP, Stable Diffusion).

什麼是 Computer Vision (CV)

Computer Vision = AI that understands images/video. Image classification, object detection, segmentation. Used in autonomous vehicles, medical imaging, AR/VR. High-demand ML specialty. L1: Image classification (CNNs, transfer learning)

🔧 工具與生態
PyTorchTensorFlowOpenCVYOLO v8/v9Segment Anything (SAM)Detectron2MMDetectionHugging Face TransformersONNXNVIDIA TAOLabelStudioRoboflow

📋 開始之前

Machine Learning AiPython

💰 各地區薪資

地區初級中級高級
USA$110,000$160,000$240,000
UK£65,000£95,000£140,000
EU€70,000€100,000€150,000
CANADACA$115,000CA$165,000CA$250,000

❓ 常見問題

Computer Vision vs NLP salary, why CV specialists earn more?
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.
Foundation models like SAM/CLIP, do I still need CNN expertise?
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.
How do I deploy CV models to edge devices (phone/robot)?
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.
Dataset bottleneck, 10k labeled images vs 1M unlabeled. What's viable?
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.
What's the difference: classification vs detection vs segmentation?
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+.
Vision Transformers vs CNNs in 2026, which should I learn?
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.
How do I get from 'Hello CV' to production-ready system?
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.

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