YOLO (Darknet): How to detect a whole directory of images?

Written by - Aionlinecourse2548 times views

To detect objects in a directory of images using YOLO (Darknet), you can use the detector function in Darknet. This function takes in the path to the directory containing the images, the path to the configuration file for YOLO, the path to the pretrained weights file for YOLO, and the path to the file where the detections should be saved.

Here's an example of how you can use the detector function to detect objects in a directory of images:

./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights /path/to/image/directory -dont_show
 -ext_output -out /path/to/detection/output/file

This will run YOLO on all the images in the specified directory, and save the detections to the specified output file.

Note that you will need to have Darknet installed and set up on your system to use this command. You can find instructions for installing and setting up Darknet on the official

Darknet GitHub page: https://github.com/pjreddie/darknet

Recommended Projects

Deep Learning Interview Guide

Topic modeling using K-means clustering to group customer reviews

Have you ever thought about the ways one can analyze a review to extract all the misleading or useful information?...

Natural Language Processing
Deep Learning Interview Guide

Medical Image Segmentation With UNET

Have you ever thought about how doctors are so precise in diagnosing any conditions based on medical images? Quite simply,...

Computer Vision
Deep Learning Interview Guide

Build A Book Recommender System With TF-IDF And Clustering(Python)

Have you ever thought about the reasons behind the segregation and recommendation of books with similarities? This project is aimed...

Machine LearningDeep LearningNatural Language Processing
Deep Learning Interview Guide

Automatic Eye Cataract Detection Using YOLOv8

Cataracts are a leading cause of vision impairment worldwide, affecting millions of people every year. Early detection and timely intervention...

Computer Vision
Deep Learning Interview Guide

Crop Disease Detection Using YOLOv8

In this project, we are utilizing AI for a noble objective, which is crop disease detection. Well, you're here if...

Computer Vision
Deep Learning Interview Guide

Vegetable classification with Parallel CNN model

The Vegetable Classification project shows how CNNs can sort vegetables efficiently. As industries like agriculture and food retail grow, automating...

Machine LearningDeep Learning
Deep Learning Interview Guide

Banana Leaf Disease Detection using Vision Transformer model

Banana cultivation is a significant agricultural activity in many tropical and subtropical regions, providing a vital source of income and...

Deep LearningComputer Vision