Key takeaways
- YOLO runs on CPUs, especially small variants exported to ONNX or OpenVINO, at lower frame rates than GPUs.
- Raspberry Pi can run small YOLO models, faster with accelerators; standard Arduinos cannot, though some vision boards run tiny detectors.
- OpenCV is an image processing library that can run YOLO models; YOLO is the detection model itself.
- YOLO struggles with tiny or crowded objects and some versions have copyleft licences.
- OBS can encode video on the CPU or the GPU; GPU encoders reduce CPU load during streaming.
Yes, YOLO can run on a CPU: small variants, such as nano and small models, run in real time or near real time on modern laptop CPUs, especially when exported to optimised formats like ONNX or OpenVINO, though GPUs remain much faster for larger models and high resolutions. Edge devices such as Raspberry Pi can run small YOLO models too. I run vision models in the browser for FaceVision, so this page covers YOLO on CPUs, Pi and Arduino, running it, YOLO versus OpenCV, its weaknesses, the slang meaning, YOLO mode in coding agents, and OBS.
Can YOLO run on a CPU?
Yes. YOLO models run on CPUs with frameworks like PyTorch, ONNX Runtime and OpenVINO. Small models at modest input sizes, such as 320 or 416 pixels, can reach usable frame rates on recent laptop CPUs; larger models and high resolutions are slow without a GPU.
Also asked as: can yolo run on cpu
To speed up CPU inference: choose the nano or small variant, reduce input resolution, export to ONNX or OpenVINO for optimised kernels, use INT8 quantisation where accuracy allows, process every second frame for video, and use multiple threads. Benchmark on your hardware, since performance varies widely between CPUs. For browser-based detection, ONNX Runtime Web can run small models directly on the user's device, the same approach FaceVision uses for privacy-preserving face detection.
How do I run YOLO?
The quickest way is the Ultralytics Python package: install it, load a pretrained model, and run it on an image, video or webcam. Results include boxes, classes and confidence scores.
Also asked as: how to run yolo
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
results = model("street.jpg", device="cpu")
results[0].show()
Run on a webcam with model(source=0, show=True). For deployment, export with model.export(format="onnx") and run with ONNX Runtime or OpenCV. To detect custom objects, fine-tune on your labelled dataset. Check the licence of the version you use before commercial deployment.
Can YOLO run on a Raspberry Pi?
Yes. Raspberry Pi 4 and 5 can run small YOLO models, typically at a few frames per second on the CPU, with better performance using optimised formats such as NCNN or ONNX, and much faster with AI accelerator add-ons designed for the Pi.
Also asked as: can yolo run on raspberry pi
Tips: use the nano model, lower input resolution, export to an optimised format, cool the Pi properly to avoid throttling, and consider an accelerator HAT for real-time needs. Typical projects include people counting, wildlife cameras and smart doorbells. Ultralytics publishes a Raspberry Pi guide with setup steps and benchmarks. Use the official Pi camera or a USB webcam, and run the detector headless, without a desktop environment, to free memory and CPU for inference.
Can you run YOLO on an Arduino?
Not on standard Arduino boards like the Uno, which lack the memory and processing power for neural networks. Some more powerful Arduino-compatible boards with cameras can run tiny, specialised detection models, but not full YOLO.
Also asked as: can you run yolo on arduino
For microcontrollers, platforms such as Edge Impulse offer compact object detection models designed for very limited hardware, detecting simple objects at low resolution. A common design uses an Arduino for sensors and motors, with detection on a Raspberry Pi, phone or nearby computer that sends results to the Arduino over serial or Wi-Fi.
YOLO vs OpenCV: which should I use?
They are not alternatives: OpenCV is a computer vision library for image processing and running models, while YOLO is an object detection model. You often use both, with OpenCV capturing and preprocessing frames and YOLO detecting objects.
If you are choosing between OpenCV's classical detectors, such as Haar cascades, and YOLO for detection, choose YOLO for accuracy and many object classes, and classical methods only for very simple, constrained tasks on weak hardware. OpenCV's DNN module can even run YOLO models exported to ONNX, combining both in one pipeline.
What is the difference between YOLO and OpenCV?
OpenCV, the Open Source Computer Vision Library, provides thousands of functions for images and video: reading cameras, filtering, geometry, drawing, classical detection and running neural networks. YOLO is a family of deep learning models specifically for detecting objects with bounding boxes.
Think of OpenCV as a toolbox and YOLO as one specialised tool. OpenCV handles everything around detection, such as resizing frames, drawing boxes, tracking and saving video, while YOLO does the recognition. Ultralytics provides its own Python tools for YOLO, but OpenCV remains common for camera handling and post-processing.
Why is YOLO bad for some tasks?
YOLO is excellent for real-time detection but has weaknesses: it can miss very small objects, struggles with heavily crowded or overlapping objects, may lose accuracy on unusual viewpoints not in training data, and some popular versions use the AGPL licence, which has obligations for commercial products.
Alternatives can be better for specific needs: two-stage detectors for small-object accuracy, segmentation models for precise outlines, and open-vocabulary detectors for objects described in text. Most weaknesses improve with fine-tuning on your own data, higher input resolution and tiling large images. Choose based on measured performance on your task.
Why is "YOLO" bad advice?
As slang, "YOLO", "you only live once", encourages impulsive risk-taking. It can be bad advice for decisions with serious, lasting consequences, such as large financial bets, unsafe stunts or careless health choices, because the phrase emphasises excitement over thinking through outcomes.
Also asked as: why yolo is bad · why is yolo bad advice
The healthier interpretation is to pursue meaningful experiences while managing risks: try new things, take calculated career risks, travel and learn, but avoid gambles that could cause irreversible harm. In finance especially, "YOLO" trades popularised online often lead to large losses. This meaning is unrelated to the YOLO detection model.
What is YOLO mode in Codex and other coding agents?
"YOLO mode" is slang for running an AI coding agent without approval prompts, letting it execute commands and edit files automatically. OpenAI's Codex CLI and other agents offer options to bypass approvals and sandboxing; check codex --help for the exact flags in your version.
Also asked as: how to yolo mode codex
It speeds up work but is risky: an agent could delete files, run harmful commands or leak secrets. Use it only in isolated environments, such as disposable containers or virtual machines, with version control, no sensitive credentials and backups. For everyday development, approval prompts and sandboxed modes provide a safer balance.
Does OBS use the CPU or GPU?
OBS Studio can use either for video encoding. The software x264 encoder uses the CPU, while hardware encoders, NVIDIA NVENC, AMD AMF and Intel Quick Sync, use the GPU's dedicated encoding hardware. OBS also uses the GPU for compositing scenes.
Also asked as: does obs use cpu or gpu
For streaming and recording while gaming or running heavy applications, GPU encoders usually reduce CPU load and stutter with good quality on modern GPUs. CPU encoding with slower presets can give better quality at low bitrates but needs a strong CPU. Choose the encoder in Settings, Output, and check the stats panel for dropped frames.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
