*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
(under submission) Bayesian Integration of a Generative Prior for Image Restoration
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p
How to Train a GAN? Tips and tricks to make GANs work
(this list is no longer maintained, and I am not sure how relevant it is in 2020) How to Train a GAN? Tips and tricks to make GANs work While research
Implementation of UNET architecture for Image Segmentation.
Semantic Segmentation using UNET This is the implementation of UNET on Carvana Image Masking Kaggle Challenge About the Dataset This dataset contains
Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
Random Erasing Data Augmentation =============================================================== black white random This code has the source code for
Collection of in-progress libraries for entity neural networks.
ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"
DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
DSEE Codes for [Preprint] DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Ch
Interpretation of T cell states using reference single-cell atlases
Interpretation of T cell states using reference single-cell atlases ProjecTILs is a computational method to project scRNA-seq data into reference sing
Implementation of Kronecker Attention in Pytorch
Kronecker Attention Pytorch Implementation of Kronecker Attention in Pytorch. Results look less than stellar, but if someone found some context where
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.
JittorVis - Visual understanding of deep learning models
JittorVis: Visual understanding of deep learning model JittorVis is an open-source library for understanding the inner workings of Jittor models by vi
The implementation of "Bootstrapping Semantic Segmentation with Regional Contrast".
ReCo - Regional Contrast This repository contains the source code of ReCo and baselines from the paper, Bootstrapping Semantic Segmentation with Regio
Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting
StochFuzz: A New Solution for Binary-only Fuzzing StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries.
Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers
Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers The repository contains the code to reproduce the experimen
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution
DASR Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021 [arXiv] Overview Requirements
End-to-end Temporal Action Detection with Transformer. [Under review]
TadTR: End-to-end Temporal Action Detection with Transformer By Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Song Bai, Xiang Bai. This repo holds the c
An energy estimator for eyeriss-like DNN hardware accelerator
Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-
FANet - Real-time Semantic Segmentation with Fast Attention
FANet Real-time Semantic Segmentation with Fast Attention Ping Hu, Federico Perazzi, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Kate Saenko , Stan Sc
CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021
crfill Usage | Web App | | Paper | Supplementary Material | More results | code for paper ``CR-Fill: Generative Image Inpainting with Auxiliary Contex