Inference using the YOLOv5 object detection model You Only Look Once (YOLO) algorithm combines classification and bounding box prediction into a single neural network. YOLO examines the entire image in one pass, capturing the contextual information of detected objects, thus reducing false-positive detections compared to methods that analyze different image regions separately, while preserving generalization capabilities. This approach known as the Single Shot Detector: where object detection and classification are performed in a single pass through the network.

You Only Look Once (V1):

bibtex @inproceedings{redmon2016you, title={You only look once: Unified, real-time object detection}, author={Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={779--788}, year={2016} } 

YOLOv5 is a an object detection model introduced by Ultralytics. For more information about YOLOv5, you can refer to the official Ultralytics repository:

Required Libraries:

pip install -r requirements.txt 

from venv:

# Create a virtual environment python3 -m venv <venvname> # Activate the virtual environment .\<venvname>\Scripts\activate pip install -r requirements.txt 

Usage

1.Clone this repository or download the script file.

2.Install the dependencies mentioned in the prerequisites section.

3.Run the script using the following command:

python ./detector_yolo5.py 

When prompted, enter the link for the the jpeg image:

Enter the path of the image: <PATH_TO_YOUR_JPEG_FILE>