AI Object Detection 2026: YOLO (60k Stars) - Count Cars, Find Defects, Track Everything for Free

Counting cars in a parking lot or detecting defects on a production line used to require a data science team. YOLO - the real-time object detection model - now runs in a few lines of Python. Here is the 2026 starter guide.

## The short answer **Ultralytics YOLO** (60,129 stars, AGPL-3.0) is the de-facto standard for real-time object detection in 2026: one pip install gives you detection, segmentation, classification, and tracking with pretrained models covering 80+ common classes. It runs on CPU, GPU, or even a Raspberry Pi. ## Quick start ```bash pip install ultralytics ``` ```python from ultralytics import YOLO # Pretrained COCO model (80 classes: person, car, dog, ...) model = YOLO("yolo11n.pt") # Detect on an image results = model("street.jpg") for r in results: for box in r.boxes: print(r.names[int(box.cls)], round(float(box.conf), 2)) # Track across video frames model.track("traffic.mp4", save=True) ``` The `yolo11n` (nano) model runs at 30+ FPS on CPU - real-time on a laptop. ## Three things you can build this weekend 1. **Traffic counter**: YOLO + a line-crossing check = car counts for a parking lot or street. 2. **Defect detection**: fine-tune on 100-500 labeled images of your product's defects (see fine-tuning with Unsloth/LoRA approaches - same principles). 3. **Security/safety alerts**: detect people in restricted zones, send a Telegram alert (see our Telegram bot guide). ## Real data on model sizes (2026) | Model | Params | Speed | Use case | |:------|:-------|:------|:---------| | yolo11n | ~2.6M | 30+ FPS CPU | Edge, Raspberry Pi | | yolo11s | ~9.4M | Fast | General | | yolo11x | ~57M | GPU | Max accuracy | ## FAQ **Is YOLO free?** The package is AGPL-3.0 (free, but copyleft); Ultralytics offers a commercial license if AGPL doesn't fit your project. **Do I need labeled data?** For 80 common classes: no, use pretrained weights. For custom objects: you need 100+ labeled images (free tools like LabelImg or Roboflow's free tier help). **Can it run on a Raspberry Pi?** Yes - the nano model runs at usable speeds on Pi 5; Pi 4 is slow but works. ## Related - [Computer Vision Tutorial with OpenCV](/post/opencv-computer-vision-tutorial-complete-guide-from-basics-to-advanced-20260723) - [Computer Vision Basics Tutorial](/post/computer-vision-basics-tutorial-for-beginners-learning-path-2026-20260723)