SemTorch

Overview

SemTorch

This repository contains different deep learning architectures definitions that can be applied to image segmentation.

All the architectures are implemented in PyTorch and can been trained easily with FastAI 2.

In Deep-Tumour-Spheroid repository can be found and example of how to apply it with a custom dataset, in that case brain tumours images are used.

These architectures are classified as:

  • Semantic Segmentation: each pixel of an image is linked to a class label. Semantic Segmentation
  • Instance Segmentation: is similar to semantic segmentation, but goes a bit deeper, it identifies , for each pixel, the object instance it belongs to. Instance Segmentation
  • Salient Object Detection (Binary clases only): detection of the most noticeable/important object in an image. Salient Object Detection

🚀 Getting Started

To start using this package, install it using pip:

For example, for installing it in Ubuntu use:

pip3 install SemTorch

👩‍💻 Usage

This package creates an abstract API to access a segmentation model of different architectures. This method returns a FastAI 2 learner that can be combined with all the fastai's functionalities.

# SemTorch
from semtorch import get_segmentation_learner

learn = get_segmentation_learner(dls=dls, number_classes=2, segmentation_type="Semantic Segmentation",
                                 architecture_name="deeplabv3+", backbone_name="resnet50", 
                                 metrics=[tumour, Dice(), JaccardCoeff()],wd=1e-2,
                                 splitter=segmentron_splitter).to_fp16()

You can find a deeper example in Deep-Tumour-Spheroid repository, in this repo the package is used for the segmentation of brain tumours.

def get_segmentation_learner(dls, number_classes, segmentation_type, architecture_name, backbone_name,
                             loss_func=None, opt_func=Adam, lr=defaults.lr, splitter=trainable_params, 
                             cbs=None, pretrained=True, normalize=True, image_size=None, metrics=None, 
                             path=None, model_dir='models', wd=None, wd_bn_bias=False, train_bn=True,
                             moms=(0.95,0.85,0.95)):

This function return a learner for the provided architecture and backbone

Parameters:

  • dls (DataLoader): the dataloader to use with the learner
  • number_classes (int): the number of clases in the project. It should be >=2
  • segmentation_type (str): just Semantic Segmentation accepted for now
  • architecture_name (str): name of the architecture. The following ones are supported: unet, deeplabv3+, hrnet, maskrcnn and u2^net
  • backbone_name (str): name of the backbone
  • loss_func (): loss function.
  • opt_func (): opt function.
  • lr (): learning rates
  • splitter (): splitter function for freazing the learner
  • cbs (List[cb]): list of callbacks
  • pretrained (bool): it defines if a trained backbone is needed
  • normalize (bool): if normalization is applied
  • image_size (int): REQUIRED for MaskRCNN. It indicates the desired size of the image.
  • metrics (List[metric]): list of metrics
  • path (): path parameter
  • model_dir (str): the path in which save models
  • wd (float): wieght decay
  • wd_bn_bias (bool):
  • train_bn (bool):
  • moms (Tuple(float)): tuple of different momentuns

Returns:

  • learner: value containing the learner object

Supported configs

Architecture supported config backbones
unet Semantic Segmentation,binary Semantic Segmentation,multiple resnet18, resnet34, resnet50, resnet101, resnet152, xresnet18, xresnet34, xresnet50, xresnet101, xresnet152, squeezenet1_0, squeezenet1_1, densenet121, densenet169, densenet201, densenet161, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn, alexnet
deeplabv3+ Semantic Segmentation,binary Semantic Segmentation,multiple resnet18, resnet34, resnet50, resnet101, resnet152, resnet50c, resnet101c, resnet152c, xception65, mobilenet_v2
hrnet Semantic Segmentation,binary Semantic Segmentation,multiple hrnet_w18_small_model_v1, hrnet_w18_small_model_v2, hrnet_w18, hrnet_w30, hrnet_w32, hrnet_w48
maskrcnn Semantic Segmentation,binary resnet50
u2^net Semantic Segmentation,binary small, normal

📩 Contact

📧 [email protected]

💼 Linkedin David Lacalle Castillo

Owner
David Lacalle Castillo
Machine Learning Engineer
David Lacalle Castillo
Image Smoothing and Blurring Using OpenCV

Image-Smoothing-and-Blurring-Using-OpenCV This repository contains codes for performing image smoothing and blurring using OpenCV. There are different

Happy N. Monday 3 Feb 15, 2022
OCR, Object Detection, Number Plate, Real Time

README.md PrePareded anaconda env requirements.txt clova AI → deep text recognition → trained weights (ex, .pth) wpod-net weights (ex, .h5 , .json) ht

Kaven Lee 7 Dec 06, 2022
An interactive interface for using OpenCV's GrabCut algorithm for image segmentation.

Interactive GrabCut An interactive interface for using OpenCV's GrabCut algorithm for image segmentation. Setup Install dependencies: pip install nump

Jason Y. Zhang 16 Oct 10, 2022
The code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes"

Mask TextSpotter A Pytorch implementation of Mask TextSpotter along with its extension can be find here Introduction This is the official implementati

Pengyuan Lyu 261 Nov 21, 2022
~1000 book pages + OpenCV + python = page regions identified as paragraphs, lines, images, captions, etc.

cosc428-structor I had an open-ended Computer Vision assignment to complete, and an out-of-copyright book that I wanted to turn into an ebook. Convent

Chad Oliver 45 Dec 06, 2022
SRA's seminar on Introduction to Computer Vision Fundamentals

Introduction to Computer Vision This repository includes basics to : Python Numpy: A python library Git Computer Vision. The aim of this repository is

Society of Robotics and Automation 147 Dec 04, 2022
Visual Attention based OCR

Attention-OCR Authours: Qi Guo and Yuntian Deng Visual Attention based OCR. The model first runs a sliding CNN on the image (images are resized to hei

Yuntian Deng 1.1k Jan 02, 2023
Text to QR-CODE

QR CODE GENERATO USING PYTHON Author : RAFIK BOUDALIA. Installation Use the package manager pip to install foobar. pip install pyqrcode Usage from tki

Rafik Boudalia 2 Oct 13, 2021
QuanTaichi: A Compiler for Quantized Simulations (SIGGRAPH 2021)

QuanTaichi: A Compiler for Quantized Simulations (SIGGRAPH 2021) Yuanming Hu, Jiafeng Liu, Xuanda Yang, Mingkuan Xu, Ye Kuang, Weiwei Xu, Qiang Dai, W

Taichi Developers 119 Dec 02, 2022
EAST for ICPR MTWI 2018 Challenge II (Text detection of network images)

EAST_ICPR2018: EAST for ICPR MTWI 2018 Challenge II (Text detection of network images) Introduction This is a repository forked from argman/EAST for t

QichaoWu 49 Dec 24, 2022
Neural search engine for AI papers

Papers search Neural search engine for ML papers. Demo Usage is simple: input an abstract, get the matching papers. The following demo also showcases

Giancarlo Fissore 44 Dec 24, 2022
A post-processing tool for scanned sheets of paper.

unpaper Originally written by Jens Gulden — see AUTHORS for more information. Licensed under GNU GPL v2 — see COPYING for more information. Overview u

27 Dec 07, 2022
Here use convulation with sobel filter from scratch in opencv python .

Here use convulation with sobel filter from scratch in opencv python .

Tamzid hasan 2 Nov 11, 2021
Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

STN-OCR: A single Neural Network for Text Detection and Text Recognition This repository contains the code for the paper: STN-OCR: A single Neural Net

Christian Bartz 496 Jan 05, 2023
Erosion and dialation using structure element in OpenCV python

Erosion and dialation using structure element in OpenCV python

Tamzid hasan 2 Nov 11, 2021
Write-ups for the SwissHackingChallenge2021 CTF.

SwissHackingChallenge 2021 : Write-ups This repository contains a collection of my write-ups for challenges solved during the SwissHackingChallenge (S

Julien Béguin 3 Jun 07, 2021
Morphological edge detection or object's boundary detection using erosion and dialation in OpenCV python

Morphologycal-edge-detection-using-erosion-and-dialation the task is to detect object boundary using erosion or dialation . Here, use the kernel or st

Tamzid hasan 3 Nov 25, 2022
Machine Leaning applied to denoise images to improve OCR Accuracy

Machine Learning to Denoise Images for Better OCR Accuracy This project is an adaptation of this tutorial and used only for learning purposes: https:/

Antonio Bri Pérez 2 Nov 16, 2022
Hiiii this is the Spanish for Linux and win 10 and in the near future the english version of PortScan my new tool on which you can see what ports are Open only with the IP adress.

PortScanner-by-IIT PortScanner es una herramienta programada en Python3. Como su nombre indica esta herramienta escanea los primeros 150 puertos de re

5 Sep 19, 2022
A python screen recorder for low-end computers, provides high quality video output.

RecorderX - v1.0 A screen recorder made in Python with the help of OpenCv, it has ability to record your screen in high quality. No matter what your P

Priyanshu Jindal 4 Nov 10, 2021