Simple object detection app with streamlit

Overview

object-detection-app

Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see how this affects predictions.

Run with Docker

From the root dir:

    docker build -t robmarkcole/object-detection-app .
    docker run -p 8501:8501 robmarkcole/object-detection-app:latest

Then visit localhost:8501

Development

Using devcontainer, see basic python example repo and more advanced repo. Use this streamlit docker template. Note you cannot docker run at the same time as the devcontainer as the ports clash.

References

I took a lot of inspiration from this article by Adrian Rosebrock.

You might also like...
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

CLOCs is a novel Camera-LiDAR Object Candidates fusion network. It provides a low-complexity multi-modal fusion framework that improves the performance of single-modality detectors. CLOCs operates on the combined output candidates of any 3D and any 2D detector, and is trained to produce more accurate 3D and 2D detection results.

Object Detection and Multi-Object Tracking
Object Detection and Multi-Object Tracking

Object Detection and Multi-Object Tracking

Object tracking and object detection is applied to track golf puts in real time and display stats/games.

Putting_Game Object tracking and object detection is applied to track golf puts in real time and display stats/games. Works best with the Perfect Prac

Auto-Lama combines object detection and image inpainting to automate object removals
Auto-Lama combines object detection and image inpainting to automate object removals

Auto-Lama Auto-Lama combines object detection and image inpainting to automate object removals. It is build on top of DE:TR from Facebook Research and

A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)
A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)

Data-Annotation-Tool How to Run this Tool? To run this software, follow the steps: git clone https://github.com/Autonomous-Car-Project/Data-Annotation

object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII
object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

赛题背景 在商品知识产权领域,知识产权体现为在线商品的设计和品牌。不幸的是,在每一天,存在着非法商户通过一些对抗手段干扰商标识别来逃避侵权,这带来了很高的知识产权风险和财务损失。为了促进先进的多媒体人工智能技术的发展,以保护企业来之不易的创作和想法免受恶意使用和剽窃,因此提出了鲁棒性标识检测挑战赛

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.
This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Skin Lesion detection using YOLO This project deal

A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

This is a simple framework to make object detection dataset very quickly
This is a simple framework to make object detection dataset very quickly

FastAnnotation Table of contents General info Requirements Setup General info This is a simple framework to make object detection dataset very quickly

Comments
  • Camera

    Camera

    Hi

    great project and an inspired one, I would like to know if I want to connect a camera or a phone camera instead of the computer camera , what should I include ? and what steps I need to add or remove?

    appreciate your feedback

    opened by oalharb 1
  • Bump opencv-python from 4.1.2.30 to 4.2.0.32

    Bump opencv-python from 4.1.2.30 to 4.2.0.32

    Bumps opencv-python from 4.1.2.30 to 4.2.0.32.

    Release notes

    Sourced from opencv-python's releases.

    4.2.0.32

    OpenCV version 4.2.0.

    Changes:

    • macOS environment updated from xcode8.3 to xcode 9.4
    • macOS uses now Qt 5 instead of Qt 4
    • Nasm version updated to Docker containers
    • multibuild updated

    Fixes:

    • don't use deprecated brew tap-pin, instead refer to the full package name when installing #267
    • replace get_config_var() with get_config_vars() in setup.py #274
    • add workaround for DLL errors in Windows Server #264
    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
Releases(v0.3)
Owner
Robin Cole
Physics PhD, python, data science, deep learning & space
Robin Cole
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

NVIDIA Research Projects 10.1k Dec 28, 2022
Powerful and efficient Computer Vision Annotation Tool (CVAT)

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 01, 2023
A time series processing library

Timeseria Timeseria is a time series processing library which aims at making it easy to handle time series data and to build statistical and machine l

Stefano Alberto Russo 11 Aug 08, 2022
The code for replicating the experiments from the LFI in SSMs with Unknown Dynamics paper.

Likelihood-Free Inference in State-Space Models with Unknown Dynamics This package contains the codes required to run the experiments in the paper. Th

Alex Aushev 0 Dec 27, 2021
Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022
MM1 and MMC Queue Simulation using python - Results and parameters in excel and csv files

implementation of MM1 and MMC Queue on randomly generated data and evaluate simulation results then compare with analytical results and draw a plot curve for them, simulate some integrals and compare

Mohamadreza Rezaei 1 Jan 19, 2022
The implementation of PEMP in paper "Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes"

Prior-Enhanced network with Meta-Prototypes (PEMP) This is the PyTorch implementation of PEMP. Overview of PEMP Meta-Prototypes & Adaptive Prototypes

Jianwei ZHANG 8 Oct 14, 2021
2.86% and 15.85% on CIFAR-10 and CIFAR-100

Shake-Shake regularization This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake

Xavier Gastaldi 294 Nov 22, 2022
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks

Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Stable Neural ODE with Lyapunov-Stable Equilibrium

Kang Qiyu 8 Dec 12, 2022
Benchmark VAE - Library for Variational Autoencoder benchmarking

Documentation pythae This library implements some of the most common (Variational) Autoencoder models. In particular it provides the possibility to pe

1.1k Jan 02, 2023
Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

MilaGraph 117 Dec 09, 2022
Classification of ecg datas for disease detection

ecg_classification Classification of ecg datas for disease detection

Atacan ÖZKAN 5 Sep 09, 2022
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

Achilles Rasquinha 1.8k Jan 05, 2023
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

ild-cnn This is supplementary material for the manuscript: "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neur

22 Nov 05, 2022
Clockwork Variational Autoencoder

Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar

Vaibhav Saxena 35 Nov 06, 2022
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

NLOS-OT Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted) Description In this reposit

Ruixu Geng(耿瑞旭) 16 Dec 16, 2022
Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

AMRBART An implementation for ACL2022 paper "Graph Pre-training for AMR Parsing and Generation". You may find our paper here (Arxiv). Requirements pyt

xfbai 60 Jan 03, 2023
Kroomsa: A search engine for the curious

Kroomsa A search engine for the curious. It is a search algorithm designed to en

Wingify 7 Jun 20, 2022