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.
💡 What You Will Learn
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 t
📜 Table of Contents
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
pip install ultralytics
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
- Traffic counter: YOLO + a line-crossing check = car counts for a parking lot or street.
- Defect detection: fine-tune on 100-500 labeled images of your product's defects (see fine-tuning with Unsloth/LoRA approaches - same principles).
- 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
❓ 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.
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