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

Ejemplo Algoritmo Viterbi - Example of a Viterbi algorithm applied to a hidden Markov model on DNA sequence

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Music Generation using Neural Networks Streamlit App

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

HomoInterpGAN - Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation

[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

This program will stylize your photos with fast neural style transfer.

Assessing syntactic abilities of BERT

Fast sparse deep learning on CPUs

A small library for creating and manipulating custom JAX Pytree classes

SIR model parameter estimation using a novel algorithm for differentiated uniformization.

Digitalizing-Prescription-Image - PIRDS - Prescription Image Recognition and Digitalizing System is a OCR make with Tensorflow

RRL: Resnet as representation for Reinforcement Learning

[ICCV 2021] Group-aware Contrastive Regression for Action Quality Assessment

Trax — Deep Learning with Clear Code and Speed

The implementation of DeBERTa

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation in TensorFlow 2

A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.