OpenCV
Images · Data
Read, transform and inspect images as part of a computer-vision pipeline.
All capabilities · Machine learning & data science
Build image models (detection/classification)
OpenCV + pretrained CNN/YOLO fine-tuning for a practical vision task.
Explore 3 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Preprocess images with OpenCV (resize, normalize, augment, color space conversions)
- Fine-tune a pretrained CNN (ResNet/EfficientNet) for an image classification task
- Fine-tune or run inference with a detection model (YOLO) for object detection
- Prepare and validate an annotated image dataset (bounding boxes) in the right format
- Evaluate detection/classification with the right metrics (mAP, IoU, precision/recall)
- Handle real-world image quality issues (lighting, blur, occlusion, low resolution)
- Optimize a vision model for reasonable inference latency, not just accuracy
Needs first: Build and train neural networks in PyTorch
Learn — free, link-checked
The few resources that matter
Read · intermediate · 60 min · docs.ultralytics.com
YOLO Object Detection & Segmentation
The docs for the most-used open detection model in Indian CV job postings -- train and evaluate a custom detector with a few lines of code. — Ultralytics
Read · intermediate · 150 min · docs.opencv.org
OpenCV-Python Tutorials
Official image-processing fundamentals (thresholding, contours, feature detection) that every CV pipeline needs before a model even runs. — OpenCV
Course · intermediate · 180 min · kaggle.com
Computer Vision
Notebook-based course on CNNs, transfer learning and data augmentation with an immediate Kaggle submission to prove the result. — Kaggle Learn
Course · intermediate · 1200 min · course.fast.ai
Practical Deep Learning for Coders
Top-down, code-first course (free videos + notebooks) that gets you to a fine-tuned, deployed image classifier before it explains the underlying math. — fast.ai
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
3 tools to explore
PyTorch
Build · Data
Train neural networks and inspect the data, loss and predictions at each stage.
Ultralytics YOLO
Images · Test
Train or run an object detector and examine missed detections and false positives.
Practice
Vehicle-type detector for Indian street scenes
Fine-tune YOLO (Ultralytics) to detect and localise the vehicle classes that actually fill an Indian road — auto-rickshaw, two-wheeler, car, bus — from a dataset you build yourself. Shoot 120–200 photos on your phone from a footpath, a bus stop or a window at different times of day, and label them in YOLO format using Roboflow's free tier or LabelImg. Train, evaluate with mAP@0.5 on a held-out split, then test on fresh photos from a location you never trained on. The lesson is how quickly lighting, occlusion and class imbalance eat a small hand-labelled dataset.
Start from
120–200 street photos you shoot on your own phone, labelled in YOLO format with Roboflow's free tier or LabelImg
Milestones
- Shoot and curate the photo set across lighting conditions, and fix the class list · ~3h
- Label everything in YOLO format and export a clean train/val split · ~4.5h
- Fine-tune YOLO, adjust augmentation, and read the per-class mAP breakdown · ~3h
- Test on fresh photos, catalogue the failures, and export the weights · ~3h
Done when
- Annotated dataset (100+ labeled images minimum) in YOLO format with a documented class list
- YOLO model fine-tuned and evaluated with mAP@0.5 reported on a held-out validation split
- Inference demonstrated on at least 5 real-world test images not in the training set, with visualized bounding boxes
- README documents failure cases observed (e.g. poor lighting, occlusion) and what would improve them
Prove it
Evidence a recruiter can check
- The per-class mAP@0.5 table with the rare class next to the common one, showing what forty examples actually buys
- Fresh test photos with predicted boxes drawn, including at least two the model gets wrong, captioned with why
- Your labelling rules — how you handled partly occluded and overlapping vehicles — since the dataset is the real work here
- Exported weights plus a one-command inference script a stranger can run on a photo of their own street
Signal it
Hand-labelled a 150-image street-scene dataset and fine-tuned YOLO to detect vehicle types — reported per-class mAP@0.5 on a held-out split and documented the occlusion and low-light cases where it fails.
Interview
Questions you'll get asked
- Walk me through fine-tuning a pretrained model for a new image classification task
- How is object detection different from classification, and what does mAP measure?
- How would you build a system to detect defective products on a manufacturing line from images?
- What data augmentation would you use for a small image dataset, and why?
- How do you handle class imbalance in an object detection dataset (rare object classes)?
- What's IoU, and how does it factor into evaluating a detection model?
- How would you reduce inference latency for a vision model running on limited hardware?