Artificial Intelligence & Data Science · 1.10

Computer Vision Engineer

Build systems that interpret visual data — from image classification to real-time object detection and generative vision models.

5–7 months5 phases30 skills

Phase 01 · 7h

Foundations

Python programming

Python programmingNumPy for array operations, file I/O, OOP patterns for building modular vision pipelines

Mathematics

Mathematicslinear algebra (matrix operations, SVD for image compression), calculus (gradients and chain rule for backpropagation), probability (Bayesian inference for detection confidence)

Digital image fundamentals

Digital image fundamentalspixels and resolution, color spaces (RGB, HSV, grayscale — converting between them), image histograms for exposure analysis, spatial and frequency domains

OpenCV basics (pip install opencv-python)

OpenCV basics (pip install opencv-python)reading/writing images (cv2.imread, cv2.imwrite), resizing and cropping, filtering (Gaussian blur, bilateral filter), edge detection (Canny with hysteresis thresholding, Sobel gradient filters), morphological operations (erosion, dilation, opening, closing — cleaning binary masks)

Phase 02 · 9h

Classical Computer Vision

Feature detection

Feature detectionSIFT (Scale-Invariant Feature Transform — detects keypoints robust to scale/rotation), ORB (Oriented FAST and Rotated BRIEF — faster alternative for real-time), Harris corner detection

Image transformations

Image transformationsaffine transforms (rotation, scaling, shearing), perspective transforms (homography — correcting document photos taken at an angle), image stitching for panoramas

Template matching, contour detection (cv2.findContours, cv2.drawContours), connected component analysis

Image segmentation

Image segmentationthresholding (Otsu's method for automatic threshold selection), watershed algorithm (marker-based segmentation), GrabCut (interactive foreground extraction)

Camera geometry

Camera geometryintrinsic parameters (focal length, principal point), extrinsic parameters (rotation, translation), camera calibration with checkerboard patterns, stereo vision basics for depth estimation

Optical flow

Optical flowLucas-Kanade (sparse tracking of feature points between frames), Farneback (dense flow estimation)

Phase 03 · 16h

Deep Learning for Vision

CNN fundamentals

CNN fundamentalsconvolution layers (how a 3×3 filter slides across an image detecting patterns), pooling (max pooling reduces spatial dimensions, provides translation invariance), stride and padding, building a CNN from scratch in PyTorch for MNIST

Architecture progression

Architecture progressionLeNet-5 (1998 — simple digit recognition), AlexNet (2012 — deeper with ReLU and dropout, won ImageNet), VGG-16 (2014 — depth matters, 3×3 filters throughout), ResNet-50 (2015 — skip connections solve vanishing gradient in deep networks), EfficientNet (2019 — compound scaling of width, depth, resolution), understanding the trade-offs (accuracy vs speed vs model size)

Transfer learning

Transfer learningload pretrained ImageNet model (torchvision.models.resnet50(weights='IMAGENET1K_V2')), replace final classification layer (model.fc = nn.Linear(2048, num_classes)), freeze early layers then optionally unfreeze for fine-tuning with small learning rate

Object detection

Object detectionYOLO v8 (pip install ultralytics — YOLO('yolov8n.pt') for inference, model.train(data='custom.yaml', epochs=100) for custom training), understanding anchor boxes, IoU (Intersection over Union), Non-Maximum Suppression (NMS), mAP evaluation metric

Semantic segmentation

Semantic segmentationU-Net (encoder-decoder with skip connections — originally for biomedical imaging), DeepLab v3+ (atrous/dilated convolutions for multi-scale context), FCN (Fully Convolutional Networks)

Instance segmentation

Instance segmentationMask R-CNN (extends Faster R-CNN with a mask branch — detects objects AND segments them individually)

Pose estimation

Pose estimationMediaPipe (Google's real-time body/hand/face landmark detection — pip install mediapipe), OpenPose concepts

Data augmentation with Albumentations

Data augmentation with AlbumentationsA.Compose([A.RandomResizedCrop(224, 224), A.HorizontalFlip(p=0.5), A.ColorJitter(brightness=0.2, contrast=0.2), A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ToTensorV2()]) — much faster and more flexible than torchvision transforms

Phase 04 · 15h

Advanced Topics

Vision Transformers (ViT)

Vision Transformers (ViT)patch embedding (split image into 16×16 patches, flatten, linear projection), positional encoding, class token, transformer encoder layers — from transformers import ViTForImageClassification; understand ViT vs CNN trade-offs (ViT needs more data but scales better)

Multi-modal models

Multi-modal modelsCLIP (contrastive learning between images and text — encode both modalities into same space, compute cosine similarity for zero-shot classification), BLIP (image captioning and VQA), Florence-2 (unified architecture for detection, segmentation, captioning)

Generative vision

Generative visionDiffusion models (forward process adds Gaussian noise, reverse process learns to denoise — Stable Diffusion architecture with U-Net, text conditioning via CLIP), VAEs (encode images to latent space, decode back), GANs (generator vs discriminator — StyleGAN for controllable face generation, Pix2Pix for image-to-image translation)

3D vision

3D visionmonocular depth estimation with MiDaS (predict depth from single images), point cloud basics (3D coordinates from LiDAR or stereo cameras), NeRF fundamentals (Neural Radiance Fields — synthesize novel views of scenes)

Video understanding

Video understandingtemporal modeling across frames, action recognition (SlowFast networks — one pathway for spatial detail, one for temporal motion), video object tracking (ByteTrack, SORT algorithms)

Edge deployment

Edge deploymentONNX export for cross-platform inference, TensorRT optimization for NVIDIA GPUs (2-5x speedup), CoreML for Apple devices, model pruning and INT8 quantization for mobile deployment

Phase 05 · 19h

Production & Capstone

Real-time inference

Real-time inferenceoptimizing for FPS (batch processing, GPU/CPU trade-offs, async inference), benchmarking latency and throughput

Annotation tools

Annotation toolsLabel Studio (open-source — bounding boxes, polygons, image classification), CVAT (video annotation), Roboflow (dataset management, augmentation, model training) — building and managing labeling pipelines with quality control

Model monitoring

Model monitoringdetecting data drift for images (changes in brightness, resolution, object distribution), performance degradation alerts

Capstone Project 1

Capstone Project 1Real-time multi-class object detection — train YOLOv8 on a custom dataset (collect 1000+ images, annotate with Label Studio), optimize with TensorRT, deploy on a video stream, measure FPS and mAP

Capstone Project 2

Capstone Project 2Medical image segmentation — U-Net for X-ray or skin lesion segmentation with Dice score evaluation, data augmentation strategy for small medical datasets, interpretability with Grad-CAM

Capstone Project 3

Capstone Project 3Image search engine — encode images with CLIP, store embeddings in a vector database (Pinecone or Chroma), build a search API (user uploads or types query → retrieve visually similar images), deploy as a web app

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