*ObjDetApp* deploys a pytorch model for object detection
____ _ _ _____ _
/ __ \| | (_) __ \ | | /\
| | | | |__ _| | | | ___| |_ / \ _ __ _ __
| | | | '_ \| | | | |/ _ \ __| / /\ \ | '_ \| '_ \
| |__| | |_) | | |__| | __/ |_ / ____ \| |_) | |_) |
\____/|_.__/| |_____/ \___|\__/_/ \_\ .__/| .__/
_/ | | | | |
|__/ |_| |_|
====================================================================
CONTENTS *Contents*
1. Introduction .................... |Introduction|
2. Prerequisites ................... |Prerequisites|
3. Usage ........................... |Usage|
3.1 WebApp ..................... |WebAppUsage|
3.2 GUIApp ..................... |GUIAppUsage|
4. Credits ......................... |Credits|
5. License ......................... |License|
====================================================================
Section 1: Introduction *Introduction*
This is a side project (or not qualified as a project) derived from a school
assignment, which focuses on the deployment of a pytorch model for object
detection, hence the name.
The model's performance is really bad but this app doesn't focus on that anyway.
You can help me perfect and package it by forking.
App tested on Linux.
====================================================================
Section 2: Prerequisites *Prerequisites*
Get trained *model_state_dict.pth* from https://drive.google.com/file/d/1oi8iIQGn0OFSRf44hWLI8kCbj5OrlkCy/view?usp=sharing and put it under this folder.
>
sudo apt install default-libmysqlclient-dev
pip install -r requirements.txt
npm install
<
====================================================================
Section 3: Usage *Usage*
WebApp:~
*WebAppUsage*
Start backend server (Default port: 5000)
>
FLASK_ENV=development FLASK_APP=server.py flask run
<
Build (Default into build/)
>
npm run build
<
Serve the webpage (Default port: 5512)
>
npm run dev
<
GUIApp:~
*GUIAppUsage*
>
python gui.py
<
====================================================================
Section 4: Credits *Credits*
ObjDetApp wouldn't be possible without these wonderful projects.
https://github.com/pallets/flask
https://github.com/pytorch/pytorch
Shout out to @sgrvinod for his great tutorial.
https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/
====================================================================
Section 5: License *License*
Copyright © 2021 Will Chao. Distributed under the MIT license.
====================================================================
vim:tw=78:ts=8:ft=help:noet:nospell
*ObjDetApp* deploys a pytorch model for object detection
Overview
StarGAN v2-Tensorflow - Simple Tensorflow implementation of StarGAN v2
Official Tensorflow implementation Open ! - Clova AI StarGAN v2 — Un-official TensorFlow Implementation [Paper] [Pytorch] : Diverse Image Synthesis f
A scikit-learn compatible neural network library that wraps PyTorch
A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look
FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks
FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At
(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
BRNet Introduction This is a release of the code of our paper Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds,
Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!
Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)
Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University
An open source implementation of CLIP.
OpenCLIP Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). The goal of this repository is to enable
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
To make the comparison with Animatable NeRF easier on the Human3.6M dataset, we save the quantitative results at here, which also contains the results of other methods, including Neural Body, D-NeRF,
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,
Deploy a ML inference service on a budget in less than 10 lines of code.
BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.
Reimplementation of Dynamic Multi-scale filters for Semantic Segmentation.
Paddle implementation of Dynamic Multi-scale filters for Semantic Segmentation.
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM
tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far
A fast Evolution Strategy implementation in Python
Evostra: Evolution Strategy for Python Evolution Strategy (ES) is an optimization technique based on ideas of adaptation and evolution. You can learn
House_prices_kaggle - Predict sales prices and practice feature engineering, RFs, and gradient boosting
House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin
Implementation for the paper 'YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs'
YOLO-ReT This is the original implementation of the paper: YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs. Prakhar Ganesh, Ya
This program writes christmas wish programmatically. It is using turtle as a pen pointer draw christmas trees and stars.
Introduction This is a simple program is written in python and turtle library. The objective of this program is to wish merry Christmas programmatical
MacroTools provides a library of tools for working with Julia code and expressions.
MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an
Code for "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection", ICRA 2021
FGR This repository contains the python implementation for paper "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection"(I
Use unsupervised and supervised learning to predict stocks
AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural n