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https://github.com/lapix-ufsc/dl-tutorials

This repository contains demonstrations done with deep learning computer vision models.
https://github.com/lapix-ufsc/dl-tutorials

computer-vision deep-learning image-classfication instance-segmentation machine-learning object-detection panoptic-segmentation semantic-segmentation

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This repository contains demonstrations done with deep learning computer vision models.

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README

          

Deep Learning tutorials
================

## Content

This package/repository is not a library, but a set of tutorials using
several libraries with models and utilities for computer vision using
deep learning.

These tutorials were built in a way that their contents are
self-contained, and that they can be used as a basis for other
experiments.

The tutorials will cover models and tools for semantic segmentation,
object detection, image classification, tracking, augmentation, model
evaluation, among other topics.

## Datasets

These tutorials are the fruit of different Lapix researchers, who
throughout their masters or doctoral degrees developed several computer
vision datasets.

Therefore, these tutorials were created from experiments using the
following datasets:

1. [CCAgT: Images of Cervical Cells with AgNOR Stain
Technique](https://doi.org/10.17632/wg4bpm33hj.2)
2. [Clouds-1000](https://doi.org/10.17632/4pw8vfsnpx.2)
3. [UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset
(v4)](https://doi.org/10.17632/dr7ydy9xbk.2)

## Authors

| Name | GitHub | Orcid |
|:----------------------------:|--------------------------------------------------|--------------------------------------------------------------|
| Aldo von Wangenheim | [@awangenh](https://github.com/awangenh) | [0000-0003-4532-1417](https://orcid.org/0000-0003-4532-1417) |
| João Gustavo Atkinson Amorim | [@johnnv1](http://github.com/johnnv1) | [0000-0003-3361-6891](https://orcid.org/0000-0003-3361-6891) |
| André Victória Matias | [@andrevmatias](https://github.com/andrevmatias) | [0000-0003-0268-0233](https://orcid.org/0000-0003-0268-0233) |