A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.

Overview

Automatic_Background_Remover

A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.

👉 https://portrait-me.herokuapp.com/

Here is the Quick look at it.

Model Details:

CNN Architecture - U-Net with Residual connections
Parameters - 8.9M
Trained on - 64,115 Images
validated on - 2693 Images
batch_size = 32
img_size = (256,256)
Trained for - 13 epochs 
Training time - 80min/epoch on GPUs by Google Colab.

Datasets used for training:

The model is trained using modified version of U-NET (https://arxiv.org/abs/1505.04597) Architecture first presented by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015. I have added Residual skip connections in U-NET Model which makes it more robust.

I can't put model architecture here because of its huuge size. view here.

Result:

Training loss - 0.038
Validation loss - 0.056

Training accuracy - 0.935
Validation accuracy - 0.907
Training meanIOU - 0.817
Validation meanIOU - 0.787

Owner
Gaurav
Artificial Intelligence practitioner with the goal of contributing to mankind.
Gaurav
Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

hongbin_xu 127 Jan 04, 2023
Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

François Chollet 16.2k Dec 30, 2022
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec This repo

Building and Urban Data Science (BUDS) Group 5 Dec 02, 2022
Hierarchical Attentive Recurrent Tracking

Hierarchical Attentive Recurrent Tracking This is an official Tensorflow implementation of single object tracking in videos by using hierarchical atte

Adam Kosiorek 147 Aug 07, 2021
PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

DRO: Deep Recurrent Optimizer for Structure-from-Motion This is the official PyTorch implementation code for DRO-sfm. For technical details, please re

Alibaba Cloud 56 Dec 12, 2022
My implementation of DeepMind's Perceiver

DeepMind Perceiver (in PyTorch) Disclaimer: This is not official and I'm not affiliated with DeepMind. My implementation of the Perceiver: General Per

Louis Arge 55 Dec 12, 2022
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
Using LSTM write Tang poetry

本教程将通过一个示例对LSTM进行介绍。通过搭建训练LSTM网络,我们将训练一个模型来生成唐诗。本文将对该实现进行详尽的解释,并阐明此模型的工作方式和原因。并不需要过多专业知识,但是可能需要新手花一些时间来理解的模型训练的实际情况。为了节省时间,请尽量选择GPU进行训练。

56 Dec 15, 2022
code from "Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity"

Code associated with the paper "Tensor decomposition of higher-order correlations by nonlinear Hebbian learning," Ocker & Buice, Neurips 2021. "plot_f

Gabriel Koch Ocker 4 Oct 16, 2022
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
Find the Heart simple Python Game

This is a simple Python game for finding a heart emoji. There is a 3 x 3 matrix in which a heart emoji resides. The location of the heart is randomized and is not revealed. The player must guess the

p.katekomol 1 Jan 24, 2022
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

Miaomiao Li 82 Jan 02, 2023
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

The Hypersim Dataset For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real i

Apple 1.3k Jan 04, 2023
A few stylization coreML models that I've trained with CreateML

CoreML-StyleTransfer A few stylization coreML models that I've trained with CreateML You can open and use the .mlmodel files in the "models" folder in

Doron Adler 8 Aug 18, 2022
An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

Karbo 45 Dec 21, 2022
Unsupervised Feature Ranking via Attribute Networks.

FRANe Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with nodes that correspond to the features

7 Sep 29, 2022
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

bottom-up-attention This code implements a bottom-up attention model, based on multi-gpu training of Faster R-CNN with ResNet-101, using object and at

Peter Anderson 1.3k Jan 09, 2023
Picasso: a methods for embedding points in 2D in a way that respects distances while fitting a user-specified shape.

Picasso Code to generate Picasso embeddings of any input matrix. Picasso maps the points of an input matrix to user-defined, n-dimensional shape coord

Pachter Lab 45 Dec 23, 2022
CVPR '21: In the light of feature distributions: Moment matching for Neural Style Transfer

In the light of feature distributions: Moment matching for Neural Style Transfer (CVPR 2021) This repository provides code to recreate results present

Nikolai Kalischek 49 Oct 13, 2022
Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

Delving into Localization Errors for Monocular 3D Detection By Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang. Intr

XINZHU.MA 124 Jan 04, 2023