fabiochiu/medium-articles huggingface dataset:
One of the first Coding with Kids students comes back as an instructor
By: Marek Brejl
Tags: Student Highlight, Teacher, Coding Classes

We are starting a series of spotlights for Coding with Kids students. The first person we are highlighting is Ronak, who started as one of the first students of our program in 2014 and kept taking classes for many years. This year we have been excited to welcome Ronak back as an assistant instructor. He is already one of our best and has great impact on the kids.

Superstar Coder: Ronak

Age 16

Ronak joined Coding with Kids’ Coder’s Ladder program seven years ago at age 9, eager to put his sharp mind to work. One of the first projects he coded was a movie program in Scratch and creating it thrilled him.

“I have so many enjoyable memories with Coding with Kids, but the best one was when my instructor told me we were going to make a platformer and that was the best project I have ever done.”

Eager to share his newfound passion with others, Ronak created the first ever Beaver Lake Middle School Coding Club which he led from grades 6–8. He loved teaching his classmates so much that he decided to pursue teaching at Coding with Kids. Ronak now supports several Coding with Kids classes as an Assistant Instructor, where he shares his love for programming with younger coders!

“Programming has taught me many life lessons, for example how to think logically and critically. Being patient and observant has been taught to me by debugging my scripts. Coding has also taught me to be innovative and creative when starting any project. With creativity, sky is the limit.”

Now in high school, Ronak is working on building out his coding portfolio for college, and hopes to pursue a career as a robotics engineer.
【華語教學路上】我適合當中文老師嗎?(看看就好之隨興測驗)
Tags: Chinese Teaching, Teachers, Mandarin, 教中文, 華語老師

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
On Teaching
By: John Gonzalez
Tags: Yoga With Adriene, Teacher Stories, On Teaching, Teachers, Essay On Teaching

Yoga has always intimidated me. Flexible, I am not. Touching my toes has meant settling for just above my knees. And the thought of going to a group yoga class is still scary.

But Yoga with Adriene has been a revelation. Adriene Mishler is a YouTuber, whose 30-day guided yoga lessons have nourished my mind, body, and soul and presented me the building blocks for better balance on and off the mat.

Each day, I’ve worked hard to fit in the daily practice of yoga, but anxious questions often fill my mind: What if I don’t have the time? What if I miss a day?

Oh, how I’ve missed the point. The core of Adriene’s teaching lies not in each individual lesson, but in Day 30. While so many instructors rev up their spoken words per minute as high as their audiences allow, Adriene pulls back. She turns her microphone off. Day 30 is silent. And our inner teacher fills the space. Day 30 is to cultivate the teacher within us all.

At some point in our lives, our teachers leave us. And if we have not internalized their lessons, this fact can be devastating. The best teachers are reliable, but taken too far, great teaching can be a tenuous crutch. Many have felt the chaos that comes when the favorite teacher is out sick and the substitute covers and is disrespected. Order is removed, and gains are lost.

Day 30 reminds me that the purpose of teaching is to leave and have student rise without you. Teaching is what happens when you’re not with students. Their education is like a door. We can choose, paint, install, and display the door, but only the student can open it. And the moment they do, we cannot be there. It’s not for us. Teaching is about letting go.
5 Ways To Become A Better Teacher
By: Michael Edwards
Tags: Teacher, School, Top 5

After being in school for many year I’ve noticed many ways to become a better teacher.

Don’t Be Too Strict

When I ask people “who is your favourite teacher”, it’s almost always the strict and harsh teacher. Being strict at tiny problems doesn’t teach the student anything, all it does is make the student hate the teacher. It’s understandable getting angry if it’s a big problem, but normally it isn’t. The teacher who is very strict is usually the most hated. If the class hates the teacher they will probably hate the subject, even if they have a new teacher the hatred for the subject won’t leave.

Don’t Be Too Nice

On the other hand being a teacher that is too nice can be even worse. If you become a teacher who doesn’t tell students off, that can cause big problems. You might become the favourite teacher of the class, but not telling students off can make others become worse, because they know they won’t get told off. Being too nice can make it very hard to control class and can make the students stop concentrating.

Be Funny

Although being too strict is bad, and being too nice is even worse, a good way to have a good balance of the two is telling jokes and being relatable. This gives the student a good memory of the teacher and usually makes them like the subject. By being fair and funny you get a good balance of the two.

Don’t Try Too Hard To Be Funny

Although being funny is good and can work a lot of the time, sometimes I see teachers try way too hard to be funny (usually older teachers). They try to think they are quite tech savvy, when they are really not. Another way to be funny and interest the student is telling interesting stories. This will interest the student and may make them enjoy the subject!

Know The Subject

I see many teachers take random subjects they barely know, just to get paid more they will take a random subject which they barely understand. Sadly if the teacher barely knows the material they will struggle to make the lesson enjoyable. Jokes and stories will only get you so far. If you don’t know much about the subject, a random student might as well teach it…
Robert Saunders Teacher — Education During Covid Lockdown And Its Impact On Child’S Health
By: Robert Saunders Teacher
Tags: Teachers, Robert Saunders Teacher, Education

Save the Children has just released a new report on the impact of covid lockout on children’s health and education.

Robert Saunders teacher Queensland The results focus on factors that affect mental health and well-being, such as physical health, physical activity, and physical and emotional health.

There is mixed evidence of educational impact, whether in the form of falling school attendance, dropout rates or dropout rates among children.

Robert Saunders Teacher — We do not yet know what the long-term effects of school cancellations will be on children’s mental health and wellbeing, or what the lockout will do to their school attendance.

There is also a need to monitor young people’s mental health in the long term and to investigate how prolonging this development affects the well-being of children and young people.

Robert Saunders teacher There is an urgent need for more research into the impact of school closures on the health and well-being of children, young people and the mentally ill, especially as health professionals engage in activities with children or young people that imprison them at work and at work, and on the impact on their education.

Robert Saunders Teacher — Child health students must focus on emerging mental health problems in children, and quarantine strategies should be developed to keep an eye on the needs of children and adolescents, as well as their health and well-being.

School needs to be done on the use of psycho — teaching materials related to audio and video material, practised mental health — health hygiene, promotion of health behaviour and access to psychosocial and educational materials related to mental health care for children or adolescents.

Save the Children calls on the government to ensure that children in schools have access to high-quality distance learning materials, that catch-up classes are offered to children who are left behind, and that all children have equal access to learning once schools reopen.

Robert Saunders teacher Queensland The government should also consider the impact of school closures on parents and find ways to help parents keep their children at home 24 hours a day and manage home work and childcare.
Robert Saunders Teacher Queensland — English What Distinguishes A Good Historian From A Bad One
By: Robert Saunders Teacher Queensland
Tags: Historians, Teachers, History, Robertsaundersteacher, Schools

Robert Saunders Teacher Queensland — English What Distinguishes A Good Historian From A Bad One

I think one of the most important aspects of learning history is learning how to think as a historian.

Robert Saunders Teacher The manual for aspiring historians often deals with the problem of forged documents, but this has rarely, if occasionally, been a problem for medieval historians.

It is also a task of the philosophy of history to recognize bad history or explanations that are valid for “bad history” but are considered “historical,” even though they are not.

Historians must define the genre to which a document belongs before they begin to address hermeneutic questions (a step they sometimes omit at their peril).

The other way to separate good historians from less good historians is to check whether the work of one or more historians has been peer-reviewed by a professional audience and published by a scholar, not a tabloid press.

Robert Saunders Teacher Queensland — Once we know that historians can provide expertise that goes beyond what commanders now have organically available, how can the task force deploy army historians? To successfully use historian’s “special skills, Army Historical Program commanders need to change their approach to historical research and analysis.

If the goal of a historian is to understand, he must ask questions about the motives that determine the actions and motivations of his colleagues.

Robert Saunders Teacher In particular, I have never fallen into the trap of thinking that we only need a series of historian’s opinions. Generalizations are claims without real evidence to support this view.

As a result, the word “historian” is so broad that it is defined by the authoritative Oxford English Dictionary as “someone who critically analyses events, as opposed to a chronicler or author. The claim that history is the study of the mind is prima facie counterproductive — intuitive, as many of us tend to think of history.

Such a definition may be useful in a broader context, but it is not useful in the narrow context in which we try to distinguish a good historian from a not — so — best historian.
Haryana TET Online Form 2020
By: Sarkari Rikti
Tags: Jobs, Teacher, Haryana Tet

Board of Secondary Education, Haryana HTET has Recently Invited to Online Application Form for the Haryana Teacher Eligibility Test, Haryana TET Online Form January 2021. Those Candidates who have Eligibility Criteria to the Following Exam Can Apply Online. Haryana TET Online Form 2020

More Details Click Here
ILLUMINATION-Curated
By: Dr. Deborah M. Vereen
Tags: Pandemic, Teachers, Parenting, Schools, Education

Parents with children enrolled in school need to appreciate the work of educators today.

It has been challenging for teachers to change their system of instructional delivery during the pandemic. They shifted from school held in-person to hybrid and online programs. Many schools that reopened were forced to close again. Yet, educators have continued to develop creative strategies to educate their students. They have also relied on ingenuity and combined it with their content-rich expertise to engage students in quality academic experiences. Even though the use of various technologies has not been perfect, they have done their best.

I wrote this article to honor educators for their perseverance and commitment. Be sure to read it if your heart is filled with gratitude for your child’s teachers.
Giggles made my Day!
By: Nesrine Mejai
Tags: Students, Student Voice, Passion, Teaching English, Teachers

I never intended to be a teacher. I became One!

I never thought that my Students will love me. I Succeeded to win their hearts and this is what really matters at the end of the day!

A memorable day yet to be completed….

I started my English class every single day with a passionate heart to leave for them a take-home note or more accurately to give them a glance of hope or a recognition..My students needed to feel loved, assured, admired, valued, recognized, and accepted in a stressful life full of ‘ You have to Do this or you must do this…!

As a teacher, I feel so sad to see some of my students suffering from the mistakes if not the errors of their elders! I mean by that, their parents, neighbors, teachers, and society. My students needed their time to enjoy and feel the moment. They are innocent souls to embrace change, hope, and challenges! They need their time to grow, learn, and blossom. A stressful life embedded within a narrow-minded society is a real hindrance for them to bloom!

My story is different!

I wanted to be a new kind of inspirational teacher!

I wanted them to enjoy inside the monotonous 4 walls!

I wanted them to feel valued, loved, heard, and accepted like friends!

I wanted them to feel free and not caged inside a class!

Students are not machines to do whatever you like!

They are gentle Souls to be respected!

They are young dreamers to be encouraged!

They are very inspiring in what they may say, tell, share, or even teach all of us!

They are good-hearted if you learn how to nurture the kindness seeds inside them!

They are respectful if you learn how to respect their ideas, voices, and opinions!

They are talented, gifted, and passionate if you ignite their wills!

They are still kids to play and enjoy their infancy if you let them live their days naturally and peacefully faraway your rigid To-Do lists!

Inspire them!

Let them enjoy their hours with you!

Let them feel free!

Let them voice what they want to say!

Let them express what they want to share!

Let them take their chances!

Let them share with you the teaching roles!

Let them feel appreciated!

Let them take a rest, play, and be crazy for a while! it won't spoil your lesson!

Let yourself live their craziness and share their living childhood!

Seeing them giggling just made my day!

My students are my daily sunshine!

My students are my everything beautiful in 24 hours time!

My students are creatively talented and inspiring to draft a memorable day!

My students are soft-hearted and a great source of wisdom, love, and appreciation!
Work Load Tyranny
By: Cara Flame 曲炎灵
Tags: Teachers, Educators, Work Life Balance, Overwork

Photo by Jo Szczepanska on Unsplash

I grew up with parents who were teachers. I remember well them working every night; marking, doing lesson prep. People were always talking about how teachers get such great holidays; I remember my mother spending hours a day through the holidays preparing lessons from the new textbook which inevitably would need to be rolled out. I grew up with a very strong and very real understanding of how a teachers’ workload was not just the day hours that everyone thinks are cushy, but far more.

My parents always told me not to be a teacher. Whatever you do, they would say, don’t become a teacher. I went my own way after high school, going into the creative arts (another field they would have preferred I didn’t go into but probably didn’t consider the need to warn me away from.) I agreed with them on one point, that I didn’t want to go into education. However, from the age of 18, this is exactly what I did. For 23 years I have worked part time or full time in education, in wide variety of fields and environments, but in education none the less. Perhaps it is in my blood, perhaps it was more environmental. Perhaps the urge to rebel caused me to unconsciously reject their admonition to never go into education. However it happened, it did, and here I am.

As it happens, I do really enjoy education, and am grateful for the opportunities I have had to work in this field. One thing that has remained constant, however, is the workload that I observed in my parents’ jobs the whole time I was growing up. I work in tertiary education, and they were in primary and secondary, but still, the same thing applies. It’s currently Friday night, 8:45pm, and I’ve finished working the week, but I still have hours of marking to do, as well as preparation and course design and reflection — more work than I could do if I continued working both weekend days full time.

Sigh.

The problem with this — well I guess there are several. One is that I never feel like I’m actually doing my job to the best of my ability. I’m constantly behind, and never preparing my classes the way I would like to. Which is detrimental to mental health. Another is that it’s exhausting and creates a terrible work life balance, which is not conducive to happy employees. But also, it’s incredibly detrimental to the students. With such a high workload, I don’t feel like I’m ever teaching the content as well as I could.

I don’t expect anything to come of this, because capitalism means that everyone in the system is constantly pushed to the edge of their capacity, squeezing every last drop of life out of them for the cause of profit. Such is the world we currently live in. I guess I’m just reflecting on something I see as a failure of this system. Especially when it comes to education. I guess as the saying goes, the system is never going to teach us what we need to dismantle it, and build something better.

Maybe we need a new kind of education system.

Sigh.
The Best Professor
By: Fatimazahraa Belkehal
Tags: Poem, Teachers, Support, Professores, Professor

The Best Professor

I felt empowered indeed

And for me you were the source

To the front you always lead

Saying the stage is yours

Though it was hard for me to stand

And to be noticed by all the class

But you made me understand

That courage is needed to progress

You knew exactly the right way

To show the girl who used to hide

I still recall my yesterday

I was your student, you was the guide

You teach, you help, you support too

And here I am passing the test

Of all the professors that I knew

You are definitely the BEST!

To my professor Khalid Lahlou
A Teacher Who Reigned in Students’ Hearts
By: Sahana Singh
Tags: Knowledge, Teachers, Sanskrit, Transformation, Education

When my grandfather Shri G.K. Timmanacharya retired in Bengaluru in the year 1949 after amassing much respect and love for his work as an educator, little did he know that the most exciting phase of his life was about to begin. But, I am getting ahead of the story.

Born on December 4, 1894, my grandfather (popularly called GKT) went on to specialise in Sanskrit grammar just like his father, his grandfather and generations before him. They were all called Vaiyakarnis or Sanskrit grammar specialists and belonged to the lineage called Arvattu Vokkalu with roots in Ahichhatra (near modern Bareilly in the state of Uttar Pradesh) from where their ancestors migrated to Karnataka on the invitation of Kadamba king Mayuravarma. These scholars were invited to settle in Karnataka in the 4th century CE in order to teach and spread Dharmic learning. My great-grandfather Shri G.V. Krishnacharya was a highly respected literary figure — author of many books in Kannada and Sanskrit as well as a mentor to Kannada scholars who went on to achieve national fame such as the iconic D.V. Gundappa.

This was first published in India Facts on Nov 14, 2018; read the full article here.
淺談mentor制度
By: Shooting Chen
Tags: Share, Life, Teachers, Mentor, Education

in The New York Times
Want to be an abacus teacher?
Tags: Teachers Training, Abacus Training, Abacus, Teachers

Want to be an abacus teacher? Well I have the perfect platform for you! The art of teaching is the art of assisting discovery. Be a part of the abacus world! Become a teacher who gives the student something to take home to think about besides homework and classwork.

AbacusMaster’s Certified Teacher training program focuses for those who are interested in abacus and are passionate towards teaching. The growing trend of abacus education in this era, gives an opportunity to begin a new business with low investment. AbacusMaster gives you an opportunity to train children; abacus based mental arithmetic as well as grows their cognitive skills. The program emphasis on a person’s teaching skills and improve the knowledge on new education technologies.

This program aims to provide you the best quality teacher training and adequate branded abacus content for an effective education. A three month training program helps you to understand the whole abacus methods, calculations and techniques as well as helps you to train children and run a business.

AbacusMaster is offering something different and the experience will be different from other abacus trainers.
［人參］外出活動，帶隊老師要做什麼？
By: 零加壹 林娘
Tags: Storytelling, Outside School, Teachers, Activity, Education

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
이미지 분류 모델 AutoML 파이프라인
By: Yoo Gyoungyoon
Tags: Image Classification, Kubeflow, Google Cloud Platform, Automl, Machine Learning

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Enrich Your App’s content with HMS ML Kit Image Classification Service
By: Yunus Emre Fırat
Tags: Image Classification, Hms Core, Hms, Ml Kit

In this article, you will have a chance to get information about HMS ML Kit Image Classification feature and its implementation. Also I try to explain why I came up with the idea of using ML Kit Image Classification feature to solve what in my demo application.

First, let’s take a look at the HMS ML Kit’s image-related Image Classification service;

The image classification service classifies elements in images into intuitive categories.

It supports both device-based and cloud-based recognition modes.

Device-based recognition refers to the process of running the detection algorithm model on the device. It supports more than 400 common image categories.

Cloud-based recognition refers to the process of calling an API on the cloud where the detection algorithm model is running. It supports 12,000 image categories.

In addition, this service allows you to customize image classification models.

With using image classification service you can enrich your application’s scenario and make it smarter. Let’s say you have a shopping application and you want to provide a feature to your users to find a product they are looking for, or a category of that product via taking a picture. Image classification service can help you to query those related products with its precise category results about the image. Also if you have a photo gallery application, integrating it with the image classification service provides for a new layer of intelligence, as images can be automatically sorted by category.

Image Classification is just one of the services of HMS ML Kit. If you want to look many other features of ML Kit and get further information about it, you can refer to below documentation.
SentiFactory is an ML low-code platform that provides tools for simplified deployment and…
Tags: Object Detection, Computer Vision, Image Classification, Artificial Intelligence, Machine Learning

SentiFactory is an ML low-code platform that provides tools for simplified deployment and monitoring of models in mobile, edge, and server field applications.

https://youtu.be/2l0aouKvR3Y
[Review] SAOL: Spatially Attentive Output Layer (Image Classification & Weakly Supervised Object Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Image Classification, Object Localization, Weakly Supervised, Deep Learning

[Review] SAOL: Spatially Attentive Output Layer (Image Classification & Weakly Supervised Object Localization)

(a) Conventional GAP Network, (b) SAOL separately obtains Spatial Attention Map and Spatial Logits, Then, Spatial Logits are weighted averaged by the Spatial Attention Map

In this story, Spatially Attentive Output Layer for Image Classification, SAOL, by Kakao Brain, is presented. In this story:

The previous GAP-FC layer is replaced with a spatially attentive output layer (SAOL) by employing a attention mask on spatial logits.

The proposed location-specific attention selectively aggregates spatial logits within a target region, which leads to not only the performance improvement but also spatially interpretable outputs .

. SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training, which can be easily plugged into existing CNNs.

This is a paper in 2020 CVPR with so far 3 citations since it is still a new paper. (Sik-Ho Tsang @ Medium)
Train a Face-Mask Detection Model in Under 5 Minutes using Lobe.ai
By: Özgür Şahin
Tags: Image Classification, Lobe, iOS, Coreml, Heartbeat

Lobe, owned by Microsoft, is a free, no-code tool to train machine learning models without technical skills. Only image classification is supported as of this writing, and an object detection model training is coming soon according to Lobe’s homepage. You can download it here by entering your basic info. It’s 608 mb.

Lobe is suitable to use it when you would like to keep your images private and train the model on your PC freely. You can also use it to create models for both Android (Tensorflow Lite) and iOS (Core ML).

Lobe welcomes you with a tour, documentation, and more, as you see below. Let’s create a new project and see how easy it is to use.

As of writing this article, you can import your image dataset only with folders so you can’t use CSV files. But Lobe does allow you label your images if they are not in labeled or categorized in folders yet.

I will use this face-mask classification dataset on Kaggle which has images categorized into two folders as with_mask and without_mask. Download this dataset and click Import and select Dataset in Lobe.

After importing your dataset, Lobe starts training immediately.

Lobe automatically splits 20% of your to dataset to test your model. Test images are a random subset of your examples are not used during training.

In the left panel you can see your dataset details. You can click on these folder names to check your images.

Lobe shows the training process live in the lower area in the left panel. There you can see how many images it predicts correctly or incorrectly.

You can leave the app while training, as it notifies you with a click sound when training finishes. For this dataset -which has 440 images- training finished under five minutes. When training finishes, you can check the results and see the correct/incorrect classifications that your model made.

Hovering over the image will show you the confidence score of the model.

To see the accuracy of your model on test images, you can select View > Test Images. It will show your model’s accuracy on test images it hasn’t seen before.

In the Play section, you can drag-drop new images or take photos with your webcam. Lobe will run the trained model with this new image and you can see how good your model does with the new images.
Hi Varshita,
By: Amit Kumar
Tags: Deep Learning, Autokeras, Python, Image Classification

Hi Varshita,

Thanks for writing a one-stop-destination article on AutoKeras as you rightly pointed out there are hardly any articles on this topic and the ones which are available deal with datasets like MNIST, CIFAR etc.

My current research work involves articulating various open-source AutoML libraries existing today and to test how they fare on different datasets especially when it comes to computer vision starting with image classification problems.

I would like to query

a) if it’s possible, to get a F1 Score or Accuracy for that matter above 90% on test dataset by running the model let’s say for 10mins. Assuming the dataset at hand is pretty common one like let’s say cats_vs_dogs.

I know for a fact that AK via NAS solves the challenge of not fighting with various SOTA NN architectures by automating this part. However, I am curious if one of the purpose of AK is to come up with better evaluation metric with reduced train time.

Secondly, have you tried using TPOT+pic2vec on image classification problems. I am currently testing it on a fairly simple dataset, cats_vs_dogs and will share my observations once I am done with it.

Best,

Amit
Simple image classification (persons, animals, other) on raspberry pi using custom model tflite (output to terminal) dividing image into 4 parts using OpenCV and TensorFlow Lite
By: Alex G.
Tags: Tensorflow Lite, Opencv Python, Image Recognition, Image Classification, Opencv

Note before you start:

So, Let’s start :)

Hardware preparation:

Software preparation:

1 Create a neural network model to predict 3 classes persons, animals, others.

I have already done it in the article below. So in this article, you do NOT NEED to DO IT.

2 Convert h5 model to tflite model

I have already done it in the article below. So in this article, you do NOT NEED to DO IT.

3 I want to divide this image into 4 parts using OpenCV

divide this image into 4 parts using OpenCV (Photo,GIF by Author) https://github.com/oleksandr-g-rock/simple-image-classification-4-divide-image-tflite/blob/main/classify_4_image_divided.jpg

For your information, I explained how I did it in this article:

4 Predict class for each part of this divided image

So, let’s implement our model to image classification (persons, animals, other) on raspberry pi using custom model tflite (output to terminal) dividing image into 4 parts using OpenCV and TensorFlow Lite

For that, you need to run the next code:



git clone #clone my repogit clone https://github.com/oleksandr-g-rock/simple-image-classification-4-divide-image-tflite.git #go to folder

cd simple-image-classification-4-divide-image-tflite

wget #download tflite modelwget https://github.com/oleksandr-g-rock/how_to_convert_h5_model_to_tflite/raw/main/animall_person_other_v2_fine_tuned.tflite #run script

python3 image_classify_divided_image.py

5 You should see something like that.

Predict class for each part of this divided image (Photo,GIF by Author) https://github.com/oleksandr-g-rock/simple-image-classification-4-divide-image-tflite/blob/main/demo_divided_4.gif

So this model predicted the absolutely right class for each part of image.

Because output was:

IMAGE R4. ... class name: animals

IMAGE L2. ... class name: other

IMAGE L1. ... class name: animals

IMAGE R3. ... class name: person

Result:

In this article, we created a solution for image classification (persons, animals, other) on raspberry pi using custom model tflite (output to terminal) dividing image into 4 parts using OpenCV and TensorFlow Lite. Code for this article is available here.
Loci ML Studio is Available
By: Loci Ml
Tags: Machine Learning, Image Recognition, AI, Image Classification, Software Development

Loci ML Studio is a cloud-based self-service platform, supporting you to tackle your image classification challenges. We focus on simplicity, high availability of our infrastructure and the security of your data. With tenant-based encryption and data centers only in Germany for your success.

Try it out for free: https://studio.loci.ml

More info on https://loci.ml
Introducing UpStride’s Open-Source Image-Classification API
Tags: Programming, Computer Vision, Image Classification, Open Source, Deep Learning

How Can You Use It?

Everything is pure TensorFlow — with its latest version. It’s super stable and reliable, and it can scale up to a data center of GPUs. We have many functions preprocessing that you can configure. The training-model part is in TensorFlow 2, where everything is neat and clean. The export part is the same — you can export in few lines of code.

You only have to write a simple configuration file to leverage the API. Then you can specify which model architecture you want, point to your data folder, list the different data-augmentation strategies, and set the hyperparameters.

On top of this, you can run it with a simple command:

Make run starts the environment

Clean neural-net architectures

Start your project with verified and tested neural-net architectures, such as MobileNet, ResNet, VGGNet, EfficientNet, nasnet, and more. All of these are verified and tested internally.

Data-augmentation strategies

Choose from a list of options — ranging from random geometric transformations to color jittering.

Data-pipeline creation

Use the latest optimization from the TensorFlow data pipeline, with TF record processing, cache management, and data parallelism already set up.

Training on multiple GPUs

Make the best use of your computing resources by choosing from a series of options, such as distributed strategy, mirror strategy, asynchronous strategy, Horovod, and more.

Export

Quickly export or save your trained model, and start deploying efficiently using TensorRT.

Reporting

Quickly collect logs from TensorBoard, and choose from a list of options for visualization.

Our code is also intended to be very modular. If you want to implement new strategies or training methods, such as architecture search, it’s relatively easy. The only part that changes is the training part. Instead of just doing a loop and fitting models, you can do two loops — one that’ll fit the model and one that’ll fit the architecture search. If you want to implement a new training paradigm, it’s also possible.

Within UpStride, we use the API to gather all of our classification projects. It helps us avoid creating long Git branches, ensures we can easily reproduce experiments, and makes sure our benchmarks are run on the same source code as TensorFlow and that any improvements made on code features impact everyone.
Build an Image-Classification Data Set in 20 Minutes
By: Samuel Ogbonna
Tags: Programming, Data Science, Image Classification, Artificial Intelligence, Python

Build an Image-Classification Data Set in 20 Minutes

Image by Gerd Altmann from Pixabay

The biggest flaw with most online deep learning courses is most of them use data that’s been cleaned and prepared for their learners. This means learners miss out on the crucial steps of building and cleaning a data set. This skill gap usually reveals itself when you have an interesting idea and you don’t have any idea how to start working on it.

I experienced this problem after completing my first data science course. The course had a section on image classification using convolutional neural networks (CNNs), and the project used for demonstration was a dog-breed classifier using images from ImageNet.

It was an interesting project, but it taught me nothing about curating a data set. I almost gave up in frustration after that course, and my progress slowed — until recently when I started taking the “Practical Deep Learning for Coders” course by fast.ai. The course is structured in a way that gives students an end-to-end view of the AI process.

In the image-classification section of the course, students are shown how to build a data set using the Bing image search API — the process returns images related to selected search terms.

This opened a door in my mind, and after a bit of research, I found a way to achieve the same thing without a need for Microsoft accounts, API keys, and all the extras involved in working with the API.

To build your own image-classification data set in less than 30 minutes, follow the steps below.
[Paper] Hide-and-Seek (Weakly Supervised Object Localization & Action Localization)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Image Classification, Object Localization, Weakly Supervised, Deep Learning

[Paper] Hide-and-Seek (Weakly Supervised Object Localization & Action Localization)

By hiding images patches randomly, we can force the network to focus on other relevant object parts in order to correctly classify the image as ’dog’.

In this story, Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization (Hide-and-Seek), by University of California, is shortly presented. In this paper:

Weakly-Supervised Object Localization (WSOL) is to localize the object while there is no object bounding box label but only the image-level label.

is to localize the object while there is no object bounding box label but only the image-level label. The main idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden.

Thus, only input image needs to be modified during training.

This is a paper in 2017 ICCV with over 300 citations. (Sik-Ho Tsang @ Medium)
Deep Learning for Image Classification — Creating CNN From Scratch Using Pytorch
Tags: Pytorch, Computer Vision, Image Classification, Deep Learning, Machine Learning

Step 5: Start Training

Finally the moment has arrived we all are waiting for i.e Training the Model

For Training and Testing I created these two helper functions.

Now Lets start the Training:

Thanks to the helper functions we created above for, we can easily start out training process using the following code snippet.

We are training the model for 50 epochs and also saving it to disk after every 10th epoch.

Here is the output that we get during training…
Data Augmentation under 2 min with Tensorflow
By: Edward Ortiz
Tags: Image Classification, Data Visualization, Machine Learning

In Machine Learning the need to work with a large amount of data to create the most reliable experiments by training a meaningful quantity of samples is a common practice in the industry. However, there are situations when the user does not have enough data to create a good ML model, so there is when certain techniques come to a place to expand the size of a training data set.

For example, let’s imagine that a Deep Neural Network is a car that needs to get to a certain destination, and to get this done, you need to add enough gas to the car. This gas can be seen as the amount of data required for the model. Then as you can see there is an analogous relation between the model and the amount of data so the model can perform well.

The amount of data required is proportional to the number of learnable parameters in the model

Data Augmentation is one technique in machine learning that apply different transformations on the available data to synthesize new data, thus expanding the amount of data so the model can work properly. Taking the example of a Deep Neural Network, the model trained to achieve high performance on complex tasks generally have a large number of hidden neurons. As the number of hidden neurons increases, the number of trainable parameters also increases.

Taking the example of an image classification application, the performance of a model without augmentation can be 57%, but when implementing data augmentation, the performance can increase up to 78%. Data augmentation can be applied using regular transformation or GANs techniques and can be applied not only to image classification but to text classification as well.

With Tensorflow 2.3.0, data augmentation can be an automated process that can be achieved by doing the following steps:

Step 1

Import tensorflow and optional tensorflow_datasets to play with random data

import tensorflow as tf

import tensorflow_datasets as tfds

import matplotlib.pyplot as plt

Step 2

Load the dataset

doggies = tfds.load('stanford_dogs', split='train', as_supervised=True)

Step 3

Use the module tf.image + the modification you want. Check more in the tf documentation

def flip_image(image):

"""flip image"""

flip = tf.image.flip_left_right(image)

return flip

Final remarks

Simple as that you can create augmented data and expand the size of your dataset if working with images. I encourage you to check the module tf.image and use all the different methods allowed so next time you don’t see that augmented data technique is a difficult thing to handle
[Paper] CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (Image Classification)
By: Sik-Ho Tsang
Tags: Artificial Intelligence, Image Classification, Regularization, Deep Learning, Data Augmentation

[Paper] CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (Image Classification)

CutMix: Patches are cut and pasted among training image

In this story, CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (CutMix), by NAVER Corp., LINE Plus Corp., and Yonsei University, is shortly presented. In this paper:

Patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches.

to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies.

This is a paper in 2019 ICCV with over 190 citations. (Sik-Ho Tsang @ Medium)
Computer Vision menggunakan Transfer Learning di Keras
By: Raihan Nugroho Jauhari
Tags: Pre Trained Model, Image Classification, Transfer Learning, Deep Learning, Machine Learning

Open to New Opportunity | Background in PHP Programming | Beginner in Data Science
Simple image classification on raspberry pi from pi-camera (in live time) using the pre-trained model mobilenet_v1 and TensorFlow Lite
By: Alex G.
Tags: TensorFlow, AI, Image Classification, Python, Machine Learning

Note before you start:

So, Let’s start :)

Hardware preparation:

Software preparation:

1 Try predict class from camera raspberry pi in live time using Tensorflow Lite.

For that, you need to run the next code:



pip3 install #install tensorflow litepip3 install https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp37-cp37m-linux_armv7l.whl git clone https://github.com/tensorflow/examples --depth 1 cd examples/lite/examples/image_classification/raspberry_pi # The script takes an argument specifying where you want to save the model files

bash download.sh /tmp

2 preparing VNC (if you have)

If you go to VNC Server Options (right-click on the VNC status icon in the top right) and check to Enable direct capture mode on the Troubleshooting page. This will allow you to see the camera output via VNC. Like on the screenshots:

3 After that run script in terminal

python3 classify_picamera.py \

--model /tmp/mobilenet_v1_1.0_224_quant.tflite \

--labels /tmp/labels_mobilenet_quant_v1_224.txt

4 You should see something like that.

Simple image classification on raspberry pi from pi-camera (in live time) using the pre-trained model mobilenet_v1 and TensorFlow Lite (Photo,GIF by Author) https://github.com/oleksandr-g-rock/simple_object_detection/blob/main/1_hd7sGxFw7fwPRoCiDQXvSQ.gif

Result:
Simpsons Characters Classification with Deep Learning toolbox in Matlab
By: Kalyani Chaganti Gudlavalleti
Tags: Newbie, Fun, Image Classification, Matlab

Introduction

With powerful deep learning frameworks like Tensorflow, Keras and PyTorch around, why would someone choose Matlab for deep learning ? While one can do much more with the above three, I find Matlab to be quite user-friendly. It is a tool where you can actually spend less time debugging and more time learning if you are a newbie. The built-in functions help ramp up the process of learning. One can experiment and visualize what they are doing easily without having to write a ton of code. I have chosen Matlab to be my first tool. As a first experiment, I have chosen to classify the fun Simpsons character images.

Get the Data

The dataset for this exercise comes from Kaggle.

The images of the different characters are stored in folders with their respective names. Matlab’s imagedatastore datatype can be used to exploit this kind of data layout to easily divide the data into train, validation and test datasets for deep learning purposes. Data augmentation can also be done on these sub-imagedatastores. Since the data is not actually loaded into the RAM until during training, imagedatastores cut down on overhead processing time and RAM space. In this experiment, I divided the dataset into 75% train, 12.5% into validation and 12.5% into test categories.

Base Network

Training was done using transfer learning using an Alexnet base network. Alexnet network is available as an add-on to the deep learning toolbox from Matlab. Similarly, there are other pre-trained networks that are available like resnet, VGG etc as add-ons that can be used to generate quick image classification or other results on any dataset.

Modify FC and Output Layers

I modified the final fully connected layer, fcfinal to give 42 outputs instead of 1000.

Visualize the modified Network Layers

Training

The training options I chose are as follows:

Training Options

Matlab opens an accuracy and loss plot to visualize training. The loss blew up pretty early when my initial learning rate was 0.01. I then changed it to 0.001 and the training went pretty smoothly. the training could’ve been continued longer for better results by increasing the validation patience. The validation accuracy and losses are plotted with the black dots in the graphs below. There is no over-fitting as can be seen from the plots below.

Training Visualizer

Network Analyzer in Matlab

Another neat feature of Matlab is the Network Analyzer where one can see the network graph and find out if there any erratic connections in it. My sequential network looked as follows.

The sequential network in network analyzer

Confusion chart on the test data

Finally, I obtained an accuracy of ~90% on my test dataset. It looks like I got pretty decent results as is evident from my confusion chart below.
Image classification in 3 easy steps
By: Vivek G
Tags: Artificial Intelligence, Image Processing, Image Classification, TensorFlow

Prepare environment

1.Install vargant from https://www.vagrantup.com/

2.clone repo “git clone https://github.com/vivekg13186/image_classification_in_3_easy_steps.git”

3.Goto the directory and start vagrant box using “vagrant up” .Then “vagrant ssh” .Now you will be in the vbox machine

Training new model

1.Now copy all the images need to be trained.Suppose you want to classify ‘cats’ and ‘dogs’ create two directory name ‘cats’ and ‘dogs’ inside ‘training_data’ directory.You can view this files inside vbox in the path ‘/vagrant/training_data’

2.To start training type ‘./train.sh’.Wait for the script to complete

Run the classifier

1.To check the classifier run ‘./find.sh <<path of the image file to check>>’ .e.g ./find.sh /vagrant/training_data/dogs/1.jpg

2.This will result in output.txt file in JSON format.

Notes: Whats is simplified here

1.Hide the complexity of CNN,Neural Network etc.. which is not required if you try to do a simple classification.

2. Tensorflow installation is not easy as give in the tensorflow website.You may end you lot of dependency issue.Vagrant simply the installation process.

3.Simple command train.sh and find.sh to reduce the number of parameter as input as they are confusing

4.This will be good base if you like to start learning tensorflow or image classification.

5.Next step you can go the python file and fine tune the parameters as you like

Read : https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0

Sample training data : http://download.tensorflow.org/example_images/flower_photos.tgz