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YOLOv5 and Seq-NMS

YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

This repo performs post-processing with Sequential NMS on yolov5 to improve results. The Seq-NMS method is adopted from "Seq-NMS for Video Object Detection".

Set-Up and Inference

Steps:
1. Install
2. Inference

Please follow commands in sections below.

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

git clone https://github.com/ShaddAhmed14/yolov5-master  # clone
cd yolov5-master
pip install -r requirements.txt  # install
Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

python detect.py --source 0  # webcam
                          vid.mp4  # video
                          path/  # directory
                          path/*.jpg  # glob
                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
# Example

python detect.py --source  'https://www.youtube.com/watch?v=P1qHv44_wLQ'