Annotate datasets with a semi-trained or fully trained YOLOv5 model

Last update: May 14, 2022

Overview

YOLOv5 Auto Annotator

Annotate datasets with a semi-trained or fully trained YOLOv5 model

Prerequisites

Ubuntu >=20.04
Python >=3.7

System dependencies

sudo apt install python3-dev python3-pip

Python dependencies

cycler==0.11.0
fonttools==4.29.0
kiwisolver==1.3.2
lxml==4.6.4
numpy==1.21.4
opencv-contrib-python==4.5.5.62
opencv-python==4.5.5.62
packaging==21.3
Pillow==9.0.0
pyparsing==3.0.7
python-dateutil==2.8.2
six==1.16.0
tqdm==4.62.3

Install with the following command -

pip3 install -r requirements.txt

Run the application

Execute annotate.py in the following format -

usage: annotate.py [-h] [--viewmode] [--imgdir IMGDIR] [--annodir ANNODIR] [--confThreshold CONFTHRESHOLD] [--nmsThreshold NMSTHRESHOLD] [--width WIDTH] [--height HEIGHT] [--onnx_path ONNX_PATH] [--labels_path LABELS_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --viewmode            Toggle View Mode
  --imgdir IMGDIR       Directory of images
  --annodir ANNODIR     Directory of annotations
  --confThreshold CONFTHRESHOLD
                        Class confidence
  --nmsThreshold NMSTHRESHOLD
                        NMS threshold
  --width WIDTH         Width of network input
  --height HEIGHT       Height of network input
  --onnx_path ONNX_PATH
                        Path to onnx file
  --labels_path LABELS_PATH
                        Path to labels file

Example -

python3 annotate.py --imgdir /home/kn1ght/Documents/images --annodir annotations --onnx_path models/YOLOv5s/yolov5s.onnx --labels_path models/YOLOv5s/coco.names --viewmode

Annotate datasets with a semi-trained or fully trained YOLOv5 model

Related tags

Overview

YOLOv5 Auto Annotator

Prerequisites

System dependencies

Python dependencies

Run the application

Owner

Akash James

Optimizing Deeper Transformers on Small Datasets

Graph Representation Learning via Graphical Mutual Information Maximization

For visualizing the dair-v2x-i dataset

A Gura parser implementation for Python

Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Python Classes: Medical Insurance Project using Object Oriented Programming Concepts

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

PyTorch implementation of Pay Attention to MLPs

An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

This is a repo of basic Machine Learning!

PyTorch common framework to accelerate network implementation, training and validation

Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021

A PyTorch Image-Classification With AlexNet And ResNet50.

This repo contains implementation of different architectures for emotion recognition in conversations.

Pytorch implementation of Cut-Thumbnail in the paper Cut-Thumbnail:A Novel Data Augmentation for Convolutional Neural Network.

Introduction to AI assignment 1 HCM University of Technology, term 211

A particular navigation route using satellite feed and can help in toll operations & traffic managemen