suryanshgupta9933/Dense-Object-Detection
Object Detection in Dense Environments using Yolov5 on SKU110K dataset and Post Quantization.
This project helps businesses accurately count and identify products in very crowded retail environments. By feeding in images of densely packed shelves or displays, it outputs the location and type of each individual item, even when many are present. Retail managers, inventory specialists, or operations engineers who need precise stock control would find this useful.
No commits in the last 6 months.
Use this if you need to perform real-time product detection and counting in high-density retail settings using devices with limited computing power, such as mobile cameras or edge devices.
Not ideal if your object detection needs involve sparse environments or require extremely high accuracy at the cost of processing speed on powerful hardware.
Stars
12
Forks
3
Language
Python
License
MIT
Category
Last pushed
Sep 12, 2023
Commits (30d)
0
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