# data[].attributes.body
https://brasil.io/dataset/genero-nomes/nomes/

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Classificação de gênero em nomes brasileiros, com base em dados do CENSO de 2010.

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Wal - Base da dados da UCI para análise de sentimentos

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Wal - Base da dados da UCI para análise de sentimentos

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https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences

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"-//W3C//DTD HTML 4.01 Transitional//EN\">

UCI Machine Learning Repository: Sentiment Labelled Sentences Data Set

Sentiment Labelled Sentences Data Set
Download : Data Folder , Data Set Description

Abstract: The dataset contains sentences labelled with positive or negative sentiment.

Data Set Characteristics:

Text

Number of Instances:

3000

Area:

N/A

Attribute Characteristics:

N/A

Number of Attributes:

N/A

Date Donated

2015-05-30

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

208959

Source:
Dimitrios Kotzias dkotzias '@' ics.uci.edu

Data Set Information:
This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015
Please cite the paper if you want to use it :)

It contains sentences labelled with positive or negative sentiment.

=======
Format:
=======
sentence score

=======
Details:
=======
Score is either 1 (for positive) or 0 (for negative)
The sentences come from three different websites/fields:

imdb.com
amazon.com
yelp.com

For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews.
We attempted to select sentences that have a clearly positive or negative connotaton, the goal was for no neutral sentences to be selected.

Attribute Information:
The attributes are text sentences, extracted from reviews of products, movies, and restaurants

Relevant Papers:
'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015

Citation Request:
'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015

Supported By:

In Collaboration With:

About ||Citation Policy ||Donation Policy ||Contact ||CML

# data[].attributes.attachment_link.description
"-//W3C//DTD HTML 4.01 Transitional//EN\"> UCI Machine Learning Repository: Sentiment Labelled Sentences Data Set Sentiment Labelled Sentences Data Set Download : Data Folder , Data Set Description Abstract: The dataset contains sentences labelled with positive or negative sentiment. Data Set Characteristics: Text Number of Instances: 3000 Area: N/A Attribute Characteristics: N/A Number of Attributes: N/A Date Donated 2015-05-30 Associated Tasks: Classification Missing Values?

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https://www.kaggle.com/sid321axn/malicious-urls-dataset

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Fernanda - Previsão de dados meteorológicos

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Fernanda - Previsão de dados meteorológicos

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https://meteonet.umr-cnrm.fr/

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Ground observations

500 ground stations measuring pressure, temperature, humidity...
Recorded every 6 min

Land-sea and relief masks

Land-sea (binary) and relief (meters) masks

Weather models

Forecasts from 2 weather models
2D and 3D parameters
Generated once a day

Precipitation radar

Radar reflectivity
Total rainfall and quality code
Recorded every 5 min

Satellite data

Cloud Type (CT), recorded every 15 min
Channels (visible, infrared), recorded every 1 hour

The data have been carefully chosen to remain reasonable in size. The full dataset weights 218GB but most packages are only a few GB. If you have a small hard drive, you can download only part of them.
See documentation.

New to MeteoNet? Check out our Toolbox!

Have a look at our toolbox which includes data samples from MeteoNet written in python language and our tutorials/documentation which help you explore and cross-check all data types.

Get MeteoNet Toolbox

Download Area

This dataset is yours to explore!

Play with it and if you send us your results, we could showcase them on this website!

Download MeteoNet

Kaggle

The data are also available on Kaggle with notebooks to help you explore and cross-check all data types!
You can contribute to challenges and/or propose yours!
Time series prediction
Rainfall nowcasting
Cloud cover nowcasting
Observation data correction
...etc

Kaggle page Tutorial

The community's work

Featured projects

You did something interesting with our dataset? Want your project to be showcased here?
Write a blog, contact us on GitHub, and we will come back to you!

Support

Need help? Checkout our documentation, post an issue on our GitHub repository or go to our Slack workspace!

Documentation GitHub Slack

Other data

Other data from METEO FRANCE

You can find other data on METEO FRANCE public data website. It features real-time, past and forecast data: in situ observations, radar observations, numerical weather models, climate data, climate forecasts and much more!

Licence

The Dataset is licenced by METEO FRANCE under Etalab Open Licence 2.0.

Reuse of the dataset is free, subject to an acknowledgement of authorship. For example:
"METEO FRANCE - Original data downloaded from https://meteonet.umr-cnrm.fr/, updated on 30 January 2020".

When using this dataset in a publication, please cite:
Gwennaëlle Larvor, Léa Berthomier, Vincent Chabot, Brice Le Pape, Bruno Pradel, Lior Perez. MeteoNet, an open reference weather dataset by METEO FRANCE, 2020

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Ground observations 500 ground stations measuring pressure, temperature, humidity... Recorded every 6 min Land-sea and relief masks Land-sea (binary) and relief (meters) masks Weather models Forecasts from 2 weather models 2D and 3D parameters Generated once a day Precipitation radar Radar reflectivity Total rainfall and quality code Recorded every 5 min Satellite data Cloud Type (CT), recorded every 15 min Channels (visible, infrared), recorded every 1 hour The data have been carefully chosen to remain reasonable in size.

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Paulo Sergio - Previsão de Tráfego da Web

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Paulo Sergio - Previsão de Tráfego da Web

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https://www.kaggle.com/c/web-traffic-time-series-forecasting

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Forecast future traffic to Wikipedia pages

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https://archive.ics.uci.edu/ml/datasets/Ozone+Level+Detection

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Ozone Level Detection Data Set
Download : Data Folder , Data Set Description

Abstract: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.

Data Set Characteristics:

Multivariate, Sequential, Time-Series

Number of Instances:

2536

Area:

Physical

Attribute Characteristics:

Real

Number of Attributes:

73

Date Donated

2008-04-21

Associated Tasks:

Classification

Missing Values?

Yes

Number of Web Hits:

156248

Source:
Kun Zhang, zhang.kun05 '@' gmail.com, Department of Computer Science, Xavier University of Lousiana
Wei Fan, wei.fan '@' gmail.com, IBM T.J.Watson Research
XiaoJing Yuan, xyuan '@' uh.edu, Engineering Technology Department, College of Technology, University of Houston

Data Set Information:
For a list of attributes, please refer to those two .names files. They use the following naming convention:

All the attribute start with T means the temperature measured at different time throughout the day; and those starts with WS indicate the wind speed at various time.

WSR_PK: continuous. peek wind speed -- resultant (meaning average of wind vector)

WSR_AV: continuous. average wind speed

T_PK: continuous. Peak T
T_AV: continuous. Average T
T85: continuous. T at 850 hpa level (or about 1500 m height)
RH85: continuous. Relative Humidity at 850 hpa
U85: continuous. (U wind - east-west direction wind at 850 hpa)
V85: continuous. V wind - N-S direction wind at 850
HT85: continuous. Geopotential height at 850 hpa, it is about the same as height at low altitude
T70: continuous. T at 700 hpa level (roughly 3100 m height)

RH70: continuous.
U70: continuous.
V70: continuous.
HT70: continuous.

T50: continuous. T at 500 hpa level (roughly at 5500 m height)

RH50: continuous.
U50: continuous.
V50: continuous.
HT50: continuous.

KI: continuous. K-Index [Web Link]
TT: continuous. T-Totals [Web Link]
SLP: continuous. Sea level pressure
SLP_: continuous. SLP change from previous day

Precp: continuous. -- precipitation

Attribute Information:
The following are specifications for several most important attributes that are highly valued by Texas Commission on Environmental Quality (TCEQ). More details can be found in the two relevant papers.

O 3 - Local ozone peak prediction
Upwind - Upwind ozone background level
EmFactor - Precursor emissions related factor
Tmax - Maximum temperature in degrees F
Tb - Base temperature where net ozone production begins (50 F)
SRd - Solar radiation total for the day
WSa - Wind speed near sunrise (using 09-12 UTC forecast mode)
WSp - Wind speed mid-day (using 15-21 UTC forecast mode)

Please refer to those two .names files.

Relevant Papers:
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008.
Discusses details about the dataset, its use as well as various experiments (both cross-validation and streaming) using many state-of-the-art methods.
A shorter version of the paper (does not contain some detailed experiments as the journal paper above) is in:
Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions. ICDM 2006: 753-764

Citation Request:
Please refer to the Machine Learning Repository's citation policy

# data[].attributes.attachment_link.description
Ozone Level Detection Data Set Download : Data Folder , Data Set Description Abstract: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.

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Wal - Dataset for classification of gender by name.

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Wal - Dataset for classification of gender by name.

# data[].attributes.body
https://archive.ics.uci.edu/ml/datasets/Gender+by+Name

# data[].attributes.attachment_link.readable_text
Abstract: This dataset attributes first names to genders, giving counts and probabilities. It combines open-source government data from the US, UK, Canada, and Australia.

Source:
Dataset creator and donator: Arun Rao, e-mail: hermesfeet '@' gmail.com, Institution: Skydeck, UC Berkeley, Berkeley, CA

Source institutional websites:
-US: https://catalog.data.gov/dataset/baby-names-from-social-security-card-applications-national-level-data
-UK: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/bulletins/babynamesenglandandwales/previousReleases
-Canada: https://www2.gov.bc.ca/gov/content/life-events/statistics-reports/bc-s-most-popular-baby-names
-Australia: https://data.gov.au/dataset/ds-sa-9849aa7f-e316-426e-8ab5-74658a62c7e6/details?q=

Data Set Information:
This dataset combines raw counts for first/given names of male and female babies in those time periods, and then calculates a probability for a name given the aggregate count. Source datasets are from government authorities:
-US: Baby Names from Social Security Card Applications - National Data, 1880 to 2019
-UK: Baby names in England and Wales Statistical bulletins, 2011 to 2018
-Canada: British Columbia 100 Years of Popular Baby names, 1918 to 2018
-Australia: Popular Baby Names, Attorney-General's Department, 1944 to 2019

Attribute Information:
Name: String
Gender: M/F (category/string)
Count: Integer
Probability: Float

Relevant Papers:
Provide references to papers that have cited this data set in the past (if any).

Citation Request:
If you have no special citation requests, please leave this field blank.

# data[].attributes.attachment_link.description
Abstract: This dataset attributes first names to genders, giving counts and probabilities. It combines open-source government data from the US, UK, Canada, and Australia.

# data[].attributes.headline
Wal  - Repositório da UCI com múltiplas bases de dados para ML.

# data[].attributes.subject
Wal  - Repositório da UCI com múltiplas bases de dados para ML.

# data[].attributes.body
https://archive.ics.uci.edu/ml/datasets.php

# data[].attributes.headline
Wal - Uso de redes convolucionais para diagnóstico médico

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Wal - Uso de redes convolucionais para diagnóstico médico

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https://radrounds.com/radiology-news/list-of-open-access-medical-imaging-datasets/

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Open access medical imaging datasets are needed for research, product development, and more for academia and industry. We hope this guide will be helpful for machine learning and artificial intelligence startups, researchers, and anyone interested at all. This is a growing list and will be periodically updated - if you know of another open medical imaging dataset, please email data@radrounds.com. Radiology Open Repositories:

NIH - 100,000 chest x-rays with diagnoses, labels, annotation

TCIA - The Cancer Imaging Archive consisting of extensive number of datasets from Lung IMage Database Consortium (LIDC), Reference Image Database to Evaluate Response (RIDER), Breast MR, Lung PET/CT, Neuro MRI scans, CT Colonoscopy, Osteoarthritis database (MIA), PET/CT phantom scans

OASIS - Cross sectional imaging MRI data

Kaggle Data Science Bowl 2017 - Lung cancer imaging datasets (low dose chest CT scan data) from 2017 data science competition

Stanford Artificial Intelligence in Medicine / Medical Imagenet - Open datasets from Stanford's Medical Imagenet

MIMIC - Open dataset of radiology reports, based on critical care patients

National Library of Medicine MedPix - Free open source database of over 12,000+ cases

SMIR - Full Body CT Scans

SMIR - High Resolution Scapular Scan (CT)

SMIR - Temporal Bone CT scans

ADNI - Alzheimer's Disease Neuroimaging Initiative with MR, PET images, genetics, cognitive tests, CSF and blood biomarkers

FITBIR - Federal Interagency Traumatic Brain Injury Research system with MRI, PET images of TBI

Cancer Digital Slide Archive - Pathology and Radiology images (excellent rad-path correlations) for GBM - Glioblastoma multiforme

Johns Hopkins Diffusion Tensor Imaging (DTI) / Lab of Brain Anatomi...- High resolution neuro-MRI scans

Grand Challenge - data from over 100+ medical imaging competitions in data science

MIDAS - Lupus, Brain, Prostate MRI datasets

In additional, image resources may span beyond actual datasets of X-Ray, MR, CT and common radiology modalities.
Non-Radiology Open Repositories (General medical images, historical images, stock images with open licenses): Sources:

# data[].attributes.attachment_link.title
List of Open Access Medical Imaging Datasets - radRounds Radiology Network

# data[].attributes.attachment_link.description
Open access medical imaging datasets are needed for research, product development, and more for academia and industry. We hope this guide will be helpful for machine learning and artificial intelligence startups, researchers, and anyone interested at all. This is a growing list and will be periodically updated - if you know of another open medical imaging dataset, please email data@radrounds.com.

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Erick - Identificação e quantificação de doenças e pestes em folhas de café utilizando imagens e deep learning

# data[].attributes.subject
Erick - Identificação e quantificação de doenças e pestes em folhas de café utilizando imagens e deep learning

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https://data.mendeley.com/datasets/yy2k5y8mxg/1

# data[].attributes.attachment_link.readable_text
Description

The dataset was developed with the purpose to evaluate deep learning algorithms for segmentation and classification. it contains images of arabica coffee leaves affected by the main biotic stresses that affect the coffee tree: leaf miner, leaf rust, brown leaf spot, and cercospora leaf spot. The images were obtained using different smartphones (ASUS Zenfone 2, Xiaomi Redmi 5A, Xiaomi S2, Galaxy S8, and iPhone 6S). The leaves were collected at different times of the year in Santa Maria of Marechal Floreano in the mountains regions of the state of Espirito Santo, Brazil. The photos were taken from the abaxial (lower) side of the leaves under partially controlled conditions and placed on a white background. The acquisition of the images was done without much criterion to make the dataset more heterogeneous. A total of 1747 images of arabica coffee leaves were collected, including healthy leaves and diseased leaves, affected by one or more types of biotic stresses. The process of biotic stresses recognition for dataset labeling was assisted by an expert and performed with the captured images. From the obtained photos were generated two datasets. A dataset with the original images of the entire leaves and a second one containing only symptoms images. Details of each dataset are described in the following. Leaf dataset: It consists of the original images of the entire leaves. The images were labeled in relation to the predominant biotic stress of each leaf and its severity. Stress severity was calculated using the symptom and leaf segmentation mask using automatic image processing methods presented in Manso et al. (2019). For certain severity ranges, labels were assigned as follows: healthy (< 0:1%), very low (0.1% - 5%), low (5% - 10%), high (10% - 15%) and very high (> 15%). Symptom dataset: This dataset was created by cropping the isolated symptoms from the original images in a way that only single stress was present in each image. A total of 2147 symptom images were cropped. Each dataset is divided in training, validation and test. GL Manso, H Knidel, RA Krohling, JA Ventura (2019), A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. arXiv preprint arXiv:1904.00742

Files

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BRACOL - A Brazilian Arabica Coffee Leaf images dataset to identification and quantification of coffee diseases and pests

# data[].attributes.attachment_link.description
The dataset was developed with the purpose to evaluate deep learning algorithms for segmentation and classification. it contains images of arabica coffee leaves affected by the main biotic stresses that affect the coffee tree: leaf miner, leaf rust, brown leaf spot, and cercospora leaf spot.

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Tarcizio - Reconhecimento Som Tosse Covid

# data[].attributes.subject
Tarcizio - Reconhecimento Som Tosse Covid

# data[].attributes.attachment_link.description
2800 audio recordings for Covid-19 classification and severity regression.

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Fernanda - Reconhecimento de sons respiratórios

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Fernanda - Reconhecimento de sons respiratórios

# data[].attributes.body
https://www.kaggle.com/vbookshelf/respiratory-sound-database/code

# data[].attributes.attachment_link.description
Use audio recordings to detect respiratory diseases.

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MaskedFace-Net is a dataset of human faces with a correctly or incorrectly worn mask (133,783 images) based on the dataset Flickr-Faces-HQ (FFHQ). The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not. Indeed, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e.g., children, old people). For these reasons, several mask wearing campaigns intend to sensitize people about this problem and good practices. In this sense, this work proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net). Realistic masked face datasets are proposed with a twofold objective: i) to detect people having their faces masked or not masked, ii) to detect faces having their masks correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To the best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards permitting mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks.

For more details about this work:

Update November 29, 2020: New referencing following the online appearing on "Smart Health", Elsevier

Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi, "MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Health, ISSN 2352-6483, Elsevier, 2020. https://doi.org/10.1016/j.smhl.2020.100144 [Preprint version available at arXiv:2008.08016]

Project leaders:
Note: project leaders equally contributed to this work.
Contributors:
MaskedFace-Net is available below:

Update January 28, 2020: Refined selection of the incorrectly masked face images.

67,049 images with Correctly Masked Face Dataset (CMFD) at 1024×1024: Go to OneDrive (19 GB)

66,734 images with Incorrectly Masked Face Dataset (IMFD) at 1024×1024: Go to OneDrive (19 GB)

To facilitate the downloading of this dataset, we suggest you to use a download manager tool or OneDrive sync client.

Update January 15, 2021: Add mirror links on Google Drive.

Zip file CMFD: Part 1 - Part 2

Zip file IMFD: Part 1 - Part 2

Metadata: Each image of the MaskedFace-Net dataset has its corresponding file naming in the FFHQ dataset. Hence, the metadata file "ffhq-dataset-v2.json" (see the FFHQ webpage) can be exploited for your processing of MaskedFace-Net.

In the following the licenses of the original FFHQ-dataset: The individual images were published in Flickr by their respective authors under either Creative Commons BY 2.0, Creative Commons BY-NC 2.0, Public Domain Mark 1.0, Public Domain CC0 1.0, or U.S. Government Works license. All of these licenses allow free use, redistribution, and adaptation for non-commercial purposes. However, some of them require giving appropriate credit to the original author, as well as indicating any changes that were made to the images. The license and original author of each image are indicated in the metadata.

The dataset itself (including JSON metadata, download script, and documentation) is made available under Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt it for non-commercial purposes, as long as you (a) give appropriate credit by citing our paper, (b) indicate any changes that you've made, and (c) distribute any derivative works under the same license. https://creativecommons.org/licenses/by-nc-sa/4.0/

The licenses of MaskedFace-Net dataset: The dataset is made available under Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt it for non-commercial purposes, as long as you

1. give appropriate credit by citing our papers:

1. Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi, "MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Health, ISSN 2352-6483, Elsevier, 2020, DOI:10.1016/j.smhl.2020.100144

2. Karim Hammoudi, Adnane Cabani, Halim Benhabiles, and Mahmoud Melkemi,"Validating the correct wearing of protection mask by taking a selfie: design of a mobile application "CheckYourMask" to limit the spread of COVID-19", CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1049-1059, 2020, DOI:10.32604/cmes.2020.011663

2. indicate any changes that you've made,

3. and distribute any derivative works under the same license. https://creativecommons.org/licenses/by-nc-sa/4.0/

Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi, "MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Health, ISSN 2352-6483, Elsevier, 2020, DOI:10.1016/j.smhl.2020.100144

@Article{cabani.hammoudi.2020.maskedfacenet, title={MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19}, author={Adnane Cabani and Karim Hammoudi and Halim Benhabiles and Mahmoud Melkemi}, journal={Smart Health}, year={2020}, url ={http://www.sciencedirect.com/science/article/pii/S2352648320300362}, issn={2352-6483}, doi ={https://doi.org/10.1016/j.smhl.2020.100144} }

Karim Hammoudi, Adnane Cabani, Halim Benhabiles, and Mahmoud Melkemi,"Validating the correct wearing of protection mask by taking a selfie: design of a mobile application "CheckYourMask" to limit the spread of COVID-19", CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1049-1059, 2020, DOI:10.32604/cmes.2020.011663

@Article{cmes.2020.011663, title={Validating the Correct Wearing of Protection Mask by Taking a Selfie: Design of a Mobile Application "CheckYourMask" to Limit the Spread of COVID-19}, author={Karim Hammoudi, Adnane Cabani, Halim Benhabiles, Mahmoud Melkemi}, journal={Computer Modeling in Engineering \& Sciences}, volume={124}, year={2020}, number={3}, pages={1049--1059}, url={http://www.techscience.com/CMES/v124n3/39927}, issn={1526-1506}, doi={10.32604/cmes.2020.011663} }

The authors would like to thank:

Jolibrain, AI software and services (March 26, 2021), "Removing face masks with JoliGAN", https://www.deepdetect.com/blog/15-face-masks-gan/

Arthur Fortes (February 4, 2021), "Building a Personalized Face Mask Detection Using OpenCV and Deep Learning)", https://fortes-arthur.medium.com/building-a-personalized-face-mask-detection-using-opencv-and-deep-learning-4aae008c95a0

OrangeTree Global, Business Intelligence and Business Analytics Training Organization (January 9, 2021), "Top Free COVID dataset for Analytics professionals", https://orangetreeglobal.com/top-free-covid-dataset-for-analytics-professionals/

Stacy Stanford, Roberto Iriondo, Pratik Shukla (January 5, 2021), "Best Public Datasets for Machine Learning and Data Science", https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f

Purva Huilgol (December 15, 2020), "Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio!", https://www.analyticsvidhya.com/blog/2020/12/top-15-datasets-of-2020-that-every-data-scientist-should-add-to-their-portfolio/

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GitHub - cabani/MaskedFace-Net: MaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ).

# data[].attributes.attachment_link.description
MaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ). - GitHub - cabani/MaskedFace-Net: MaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ).

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Erick - Reconhecimento de placas de carro

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Erick - Reconhecimento de placas de carro

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https://www.kaggle.com/andrewmvd/car-plate-detection

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Paulo Sergio - Visão Computacional - OpenCv

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Paulo Sergio - Visão Computacional - OpenCv

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Informações sobre o curso acesse aqui.

Este repositório reúne todos os notebooks, imagens, modelos e demais materiais necessário para a condução das aulas e revisão das mesmas.

Utilize as discussões ou mesmo crie issues se precisar de alguma informação.

Como é um repositório público, aceito eventuais Pull Requests!

Nas aulas podemos utilizar o Google Colab, os Notebooks do Kaggle ou a própria distribuição local Anaconda, com uso do Jupyter Notebook, que há vem instalado nesta distribuição. Você também pode usar até mesmo o VSCode, escolha o ambiente que mais adeque ao seu estilo!

Para instalar o Anaconda, acesse a sessão de Downloads do Anaconda.

Tanto o Google Colab ou Kaggle podem ser acessados diretamente dos respectivos sites.

Para quem for usar Colab ou Kaggle, use o badge de cada um. Eles possuem um link que já abre direto em cada plataforma, levando em consideração as particularidades de cada ambiente.

Em algumas aulas poderá ser utilizado o streaming de vídeo de câmeras, que somente funciona em instalações locais. Tanto Google Colab quanto Kaggle ainda não suportam câmeras no modo ao vivo (exceto Colab que suporte imagens estáticas) por serem ambientes virtualizados.

Veja esta rápida introdução do uso de câmeras com o OpenCV em MacOS e Windows. Guarde esse pequeno guia para futuros usos, pois no MacOS as coisas funcionam um pouco diferente do Windows e costumam travar 😕 .

OpenCV 3.4.3 (conda install -c conda-forge opencv==3.4.3)

Keras 2.3.1 (conda install keras==2.3.1)

Matplotlib 3.1.3 (conda install matplotlib==3.1.3)

Seaborn 0.0.10 (conda install -c conda-forge seaborn==0.10.0)

Imutils 0.5.3 (conda install -c conda-forge imutils==0.5.3)

Scikit Learn 0.22.1 (conda install scikit-learn==0.22.1)

Scipy 1.4.1 (conda install scipy==1.4.1)

Pytorch 1.4.0 (conda install -c pytorch pytorch==1.4.0)

No Google Colab todas as dependências já estão instaladas. Já no Kaggle está indicando como instalar as dependências, sem dificuldades. 😄

Aulas no programa atualizado da disciplina:

1. Introdução do OpenCV

2. Instalação

3. Formação de imagens

4. Representação de cores

5. Histograma

6. Construção de imagens

Técnicas para segmentar e extrair artefatos e regiões de interesse de imagens ✂️

1. Aguçamento

2. Binarização

3. Dilatação e Erosão

4. Deteção de Borda

5. Contornos

6. Identificação de Formas

1. Classificadores em cascata de Haar

2. Classificador facial DLib

3. Análise Facial

4. Marcos Faciais

1. Reconhecimento de faces

2. Reconhecimento de imagens utilizando redes neurais profundas

3. Técnicas de transferência de aprendizado (transfer learning)

4. Reconhecimento de objetos em tempo real com YOLO You Only See Once.

1. Treinamento de modelo customizado baseado em Yolo

2. Transformadas de Rough

3. Descritores de imagem ORB

Solução

Solução

Projetos de conclusão da disciplina aplicados.

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GitHub - michelpf/fiap-ml-visao-computacional: Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP

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Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP - GitHub - michelpf/fiap-ml-visao-computacional: Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP