Learning Roadmap
How to Become a AI Computer Vision Engineer
A step-by-step, phase-based learning path from beginner to job-ready AI Computer Vision Engineer. Estimated completion: 10 months across 5 phases.
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Foundations of Computer Vision & Deep Learning
8 weeksGoals
- Master Python, NumPy, and image manipulation with OpenCV and Pillow
- Understand CNN architecture, backpropagation, and loss functions for vision tasks
- Implement image classification from scratch using PyTorch on CIFAR-10/ImageNet subsets
Resources
- Stanford CS231n (Convolutional Neural Networks for Visual Recognition)
- Fast.ai Practical Deep Learning for Coders - Part 1
- OpenCV official documentation and tutorials
- Book: Deep Learning for Vision Systems (Mohamed Elgendy)
MilestoneTrain a ResNet classifier achieving >90% accuracy on CIFAR-10 and deploy it as a Gradio demo
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Detection, Segmentation & Advanced Architectures
10 weeksGoals
- Implement and fine-tune object detection models (YOLOv8, Faster R-CNN)
- Build semantic and instance segmentation pipelines (U-Net, Mask R-CNN, SAM)
- Learn annotation workflows, dataset management, and augmentation strategies
Resources
- Ultralytics YOLOv8 documentation and tutorials
- HuggingFace Vision Transformer tutorials
- Roboflow blog and free annotation platform
- Papers: DETR, Segment Anything, DINOv2
MilestoneBuild a custom object detection model on a self-annotated dataset with mAP > 0.75
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Model Optimization & Edge Deployment
8 weeksGoals
- Learn model export to ONNX and TensorRT optimization
- Deploy models on NVIDIA Jetson and mobile devices (Core ML, TFLite)
- Implement real-time video inference with DeepStream or custom pipelines
Resources
- NVIDIA TensorRT Developer Guide
- NVIDIA Jetson AI Fundamentals course
- ONNX Runtime documentation
- Apple Core ML documentation
MilestoneDeploy a YOLO model on a Jetson Nano achieving >15 FPS on a live camera feed
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MLOps, Production Systems & Video Analytics
8 weeksGoals
- Set up CI/CD pipelines for model training, testing, and deployment
- Implement monitoring, drift detection, and automated retraining triggers
- Build multi-object tracking and video analytics systems
Resources
- MLOps Specialization (DeepLearning.AI on Coursera)
- Weights & Biases MLOps course
- ByteTrack / BoT-SORT multi-object tracking papers
- Docker + Kubernetes for ML deployment guides
MilestoneShip an end-to-end vision pipeline with automated retraining, A/B testing, and production monitoring
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Specialization & Generative Vision
6 weeksGoals
- Explore 3D vision, depth estimation, and NeRF-based reconstruction
- Learn diffusion models for image generation and synthetic data creation
- Study multimodal models (CLIP, LLaVA, GPT-4V) and their vision applications
Resources
- Papers: DALL·E 2, Stable Diffusion, CLIP, LLaVA, Gaussian Splatting
- HuggingFace Diffusers library documentation
- OpenAI Vision API documentation
- NVIDIA NeRF resources
MilestoneBuild a multimodal application combining vision-language models with custom fine-tuning
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
Real-Time Face Mask Detector
BeginnerBuild a YOLOv8-based classifier that detects whether a person is wearing a mask from a live webcam feed. Includes data collection, annotation with Roboflow, training, and deployment as a Gradio app.
Custom Image Segmentation Pipeline
BeginnerTrain a U-Net model on a medical imaging dataset (e.g., brain MRI tumors) to perform pixel-wise segmentation. Practice data augmentation with Albumentations and evaluation with Dice score and IoU.
Vehicle Detection and Counting System
IntermediateBuild a traffic surveillance system that detects, tracks, and counts vehicles across lanes in a video stream. Uses YOLOv8 for detection and ByteTrack for multi-object tracking.
Industrial Defect Inspection with Anomaly Detection
IntermediateTrain an autoencoder-based anomaly detector on normal manufacturing images only. At inference, flag anomalous patches using reconstruction error. Include a dashboard for defect localization.
Edge-Deployed Object Detection on Jetson Nano
IntermediateExport a trained YOLOv8 model to ONNX, optimize with TensorRT, and deploy on an NVIDIA Jetson Nano with a USB camera. Measure and optimize FPS, latency, and memory usage.
Zero-Shot Image Search with CLIP
IntermediateBuild a semantic image search engine using CLIP embeddings. Index a large image dataset and allow users to search with natural language queries. Deploy as a FastAPI service.
Synthetic Data Generation with Stable Diffusion
AdvancedUse Stable Diffusion and ControlNet to generate synthetic training images for a rare object detection task. Evaluate model performance trained on synthetic vs. real data and create a hybrid dataset.
End-to-End MLOps Vision Pipeline
AdvancedBuild a complete MLOps pipeline: DVC for data versioning, GitHub Actions for CI/CD, W&B for experiment tracking, Docker for containerization, and Kubernetes for serving. Deploy a vision model with monitoring and automated retraining triggers.
Multi-Modal Visual Question Answering System
AdvancedFine-tune a vision-language model (e.g., LLaVA or BLIP-2) on a custom domain dataset to answer questions about images. Build an interactive chatbot interface with streaming responses.
Ready to Start Your Journey?
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