Drone detection using YOLOv5

Last update: Dec 20, 2022

Related tags

Overview

This drone detection system uses YOLOv5 which is a family of object detection architectures and we have trained the model on Drone Dataset.

Overview

Install

Python >= 3.6.0 required with all requirements.txt dependencies installed:

$ git clone https://github.com/tusharsarkar3/Detect_Drone.git
$ pip install -r requirements.txt

Training

The structure of the file system is of great importance here so these images will show you the correct way of organizing it. The main folder named datasets should be on the same level as this repository. The next steps are elaborated in the images:

The two folders with images and labels respectively should be inside the dataset folder.

The images directory should contain the training images and the validation images respectively.

The labels directory should contain the training labels and the validation labels respectively.

Run commands below to reproduce results on Drone Dataset dataset..

$ $ python train.py --img 640 --batch 16 --epochs 15 --data coco128.yaml --weights yolov5s.pt

Check out YOLOv5 for more information.

Inference

$ python detect.py --weights 'path to the best set of weights' --source 0  # webcam       
                                                                        file.jpg  # image 
                                                                        file.mp4  # video
                                                                        path/  # directory
                                                                        path/*.jpg  # glob
                                                                        'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                                                                        'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

The results will be stored in a new directory named run which will be on the same level as the root directory.

Check out YOLOv5 for more information.

Drone detection using YOLOv5

Related tags

Overview

Overview

Results:

Developed with ❤️ by Tushar Sarkar

Owner

Tushar Sarkar

My usage of Real-ESRGAN to upscale anime, some test and results in the test_img folder

Puzzle-CAM: Improved localization via matching partial and full features.

Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On

Official repo for QHack—the quantum machine learning hackathon

[NeurIPS 2021] Garment4D: Garment Reconstruction from Point Cloud Sequences

Kohei's 5th place solution for xview3 challenge

ISNAS-DIP: Image Specific Neural Architecture Search for Deep Image Prior [CVPR 2022]

GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images

Visual Question Answering in Pytorch

A curated list of awesome Machine Learning frameworks, libraries and software.

Llvlir - Low Level Variable Length Intermediate Representation

Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

Original code for "Zero-Shot Domain Adaptation with a Physics Prior"

Catalyst.Detection

Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion

Lightweight plotting to the terminal. 4x resolution via Unicode.

PyTorch implementation of Pay Attention to MLPs