YOLO (You Only Look Once) represents a state-of-the-art, real-time object detection framework that has been widely implemented across domains such as surveillance, robotics, and autonomous navigation.

The release of YOLOv12 introduces substantial enhancements in computational efficiency and detection accuracy.

System Requirements and Prerequisites

Before proceeding with the installation and execution of YOLOv12, ensure that your macOS system is equipped with the following software components:

1. OpenCV: Essential for image processing and computer vision tasks.
2. Git: Required for repository cloning and version control.
3. Homebrew (optional): A package management system for streamlined dependency management.

pip: Python’s package management system; verify installation with:

pip3 --version

Python (≥3.x): Confirm its presence via:

python3 --version

Step 1: Installation of Required Dependencies

YOLOv12 necessitates several foundational dependencies, which can be installed using either Homebrew or pip.

Installation via Homebrew

If Homebrew is available, install OpenCV with:

brew install opencv

Installation via pip

For environments where Homebrew is unavailable or undesirable, install OpenCV through pip:

pip3 install opencv-python

Additionally, install auxiliary dependencies such as NumPy:

pip3 install numpy

Step 2: Cloning the YOLOv12 Repository

Obtain the YOLOv12 source code by cloning its official GitHub repository:

git clone https://github.com/ultralytics/yolov12.git

Move into the repository’s root directory:

cd yolov12

Step 3: Installing YOLOv12 Dependencies

YOLOv12 relies on the PyTorch deep learning framework alongside various auxiliary libraries. Install the requisite dependencies with:

pip3 install -r requirements.txt

Step 4: Downloading YOLOv12 Pretrained Models

To leverage pre-trained models for object detection, execute the following command to download the necessary model weights:

python3 models/download.py

Step 5: Executing YOLOv12 for Object Detection

Following successful installation, YOLOv12 can be applied to both image and video sources.

Object Detection on a Static Image

python3 detect.py --source path/to/your/image.jpg --weights yolov12.pt

Replace path/to/your/image.jpg with the actual path to the image file.

Object Detection on a Video File

python3 detect.py --source path/to/your/video.mp4 --weights yolov12.pt

Replace path/to/your/video.mp4 with the actual path to the video file.

Real-Time Object Detection via Webcam

python3 detect.py --source 0 --weights yolov12.pt

This command utilizes the default webcam as the video feed source.

Storing Detection Results

python3 detect.py --source path/to/your/image.jpg --weights yolov12.pt --save-txt --save-conf

This variant of the command saves both bounding box coordinates and confidence scores to a text file.

Batch Processing Multiple Images

python3 detect.py --source path/to/your/directory --weights yolov12.pt

This facilitates batch processing of all images within the specified directory.

Step 6: Visualization and Post-Processing

The detected objects are annotated with bounding boxes and classification labels. To view the processed outputs, navigate to the designated results directory and open the images or videos using standard media viewers.

Troubleshooting and Optimization Strategies

• Unresolved Dependencies: If missing dependencies trigger errors, attempt reinstalling the requisite libraries.
• Download Failures: Verify internet connectivity or manually acquire pre-trained models from the official repository.
• Suboptimal Performance: macOS systems lacking a discrete GPU may exhibit performance bottlenecks. If feasible, consider executing YOLOv12 on a high-performance computing cluster or cloud-based service optimized for deep learning.

Future Research and Development Considerations

The YOLO framework is undergoing continuous refinements, with subsequent iterations expected to further enhance model efficiency and predictive accuracy. Researchers and developers should remain vigilant for updates to the YOLO ecosystem to maximize performance gains.

Additional Recommendations

• Optimizing Performance: On Apple Silicon (M1/M2), consider employing CPU-optimized configurations for enhanced compatibility.
• Comparative Analysis: Evaluate YOLOv12 alongside prior versions such as YOLOv7 and YOLOv8 to determine optimal configurations for specific applications.
• Customization: Modify detection parameters or integrate YOLOv12 into larger machine learning pipelines for domain-specific tasks.

Conclusion

The implementation of YOLOv12 on macOS necessitates systematic installation of dependencies, acquisition of model weights, and configuration of runtime parameters.

References

1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
3. Run Microsoft OmniParser V2 on Ubuntu : Step by Step Installation Guide
4. Run YOLOv12 on Linux / Ubuntu: Step-by-Step Installation Guide
5. Run YOLOv12 on Windows: Step-by-Step Installation Guide