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YOLOV3 Object Detection With Opencv

YOLOV3 Object Detection With Opencv is a Python command-line project that uses pretrained YOLOv3 models with OpenCV to detect objects in local images, video files and live webcam input.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

YOLOV3 Object Detection With Opencv — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you have YOLOv3 model weights and configuration files and want to run local image, video or webcam inference from a Python CLI.

Who it is for

  • Developers and computer-vision learners who want a command-line example for applying pretrained YOLOv3 to images or video.
  • Users who want to explore object detection from a webcam feed.

Documented uses

  • Run object-detection inference on a local image.
  • Process a local video file for object detection.
  • Apply object detection to live webcam input.
Before you choose

Requirements & limits

  • The project requires YOLOv3 weights and configuration files; its README expects them under yolov3-coco unless paths are specified.
  • Image and video inference take local file paths, while running the script without a path option is the documented webcam mode.
  • The repository states that its code is distributed under the MIT License.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run detection on a local image

Use the project’s documented CLI command to perform inference on an image file.

Before you start

  • A local image file
  • The repository cloned locally
  • YOLOv3 weights and configuration files in the yolov3-coco directory, or their paths specified when calling the script

Steps

  1. Clone the repository and change into its directory.

  2. Place the model weights and configuration files in the yolov3-coco directory, or note their alternate paths.

  3. Run the command below with the local image path.

    Documented command
    python3 yolo.py --image-path='/path/to/image/'

  4. Use yolo.py --help for further command-line details.

Expected outcome

The script performs object-detection inference on the specified local image.

Guide limitations

  • The README requires YOLOv3 weights and configuration files; it does not document installation steps for Python dependencies.
Documentation 1 ↗
Source-based practical guide

Run detection on a local video

Use the project’s documented CLI command to perform inference on a video file.

Before you start

  • A local video file
  • The repository cloned locally
  • YOLOv3 weights and configuration files in the yolov3-coco directory, or their paths specified when calling the script

Steps

  1. Clone the repository and change into its directory.

  2. Place the model weights and configuration files in the yolov3-coco directory, or note their alternate paths.

  3. Run the command below with the local video path.

    Documented command
    python3 yolo.py --video-path='/path/to/video/'

  4. Use yolo.py --help for further command-line details.

Expected outcome

The script performs object-detection inference on the specified local video.

Guide limitations

  • The README requires YOLOv3 weights and configuration files; it does not document installation steps for Python dependencies.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
iArunava/YOLOv3-Object-Detection-with-OpenCV · Official homepage title · retained in its source language
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