fabiochiu/medium-articles huggingface dataset:
Describing what you see
By: Hariraj K
Tags: Image Classification, Neural Networks, Image Recognition

We deal with a variety of instances where we have to describe a scene or come up with a text sequence corresponding to an image. And no I am not speaking just about captioning your Instagram posts or helping your 3rd grader with his/her English assignments.

Let us talk sets

Essentially we are trying to define a function that maps an image to a sequence of texts. This function need not be one-one or onto. Although that depends on the context.

Usually, this is achieved using reinforcement learning. Obviously the ingredients for this recipe are more or less the same.

Any reinforcement training regime uses the same core philosophy. You have a set of inputs that you transform into a sequence of numbers. You perform operations on them to obtain another sequence that is mapped into your required output.

Diving into potential implementations

Most of these approaches use encoder-decoder architecture. The encoder is usually an image-based algorithm that creates ‘Image embedding’ and the decoder is a text generation algorithm. A hidden layer is sort of a bridge that maps both the layers.

Selecting an algorithm for ‘Image to Text’ boils down to one’s choice of algorithm for the encoder and the decoder layer.

‘Show and Tell’, ‘Show,Attend and Tell’

The most common approach to map an image to a single text sequence (Which will be more clear when we discuss Unet )is the Show and Tell algorithm. The encoder uses Alexnet (A popular CNN architecture to create Image Embeddings). The output is fed into a decoder which is primarily a text generation algorithm such as LSTM.

Modifications have been done to this approach by using other algorithms such as Resnet and by using transformers in the decoder instead of traditional LSTMS.

Links:

Show and Tell: A Neural Image Caption Generator

Attention Is All You Need

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Dank Learning: Generating Memes Using Deep Neural Networks

Do it for the ‘gram: Instagram-style Caption Generator

Connected Papers | Find and explore academic papers

Another approach I came across recently are Image Transformers.

Using Image Transformers

Transformers have been game changing in the case of Natural Language Processing to the point that it has been applied to other domains. One such domain being Image Processing. Image Transformers use the attention model to identify important domains in an image.

I am aware of other implementations that uses Unet for Image Captioning. However, I am yet to read those papers and will edit it here as I go. I would love for you guys to provide feedback on this.
[Paper] MobileNetV3: Searching for MobileNetV3 (Image Classification)
By: Sik-Ho Tsang
Tags: Mobilenet, Artificial Intelligence, Image Classification, Deep Learning, Neuralarchitecturesearch

[Paper] MobileNetV3: Searching for MobileNetV3 (Image Classification)

In this story, Searching for MobileNetV3, by Google AI, and Google Brain, is presented. In this paper:

MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS), i.e. MnasNet , complemented by the NetAdapt algorithm .

complemented by the . Then it is subsequently improved through novel architecture advances.

This is a paper in 2019 ICCV with over 400 citations. (Sik-Ho Tsang @ Medium)
HMS ML Kit Image Classification
Tags: Image Classification, Huawei, Hms, Android, Machine Learning

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Salesforce Einstein Vision Integration with Salesforce App
By: Khadke Chetan
Tags: AI, Image Classification, Salesforce

Salesforce provides various options to create an app with a Lighting App builder in less than 10 mins. The Salesforce App comes up with a great ability to interact with multiple different services of salesforce like Salesforce Einstein. In this article, I will walk you through, how you can integrate the Salesforce Einstine component to Salesforce App. Please consider reading this blog to create a custom Image classification using Salesforce Einstein.

Photo by Kevin Ku on Unsplash

Goal

We want to build the salesforce lighting application for tracking down the car repairing service. Our major goal behind this activity is to help the car garage people effectively manage their schedules with minimum input from users.

ML Perspective

When a new car comes for service/repair in the garage, the app automatically categorizes car images (clicked by Camera) based on the car type and model.

Prerequisite:

Einstein vision API knowledge Einstein vision trained/pre-trained modelId (Check out this blog to create one) Access token to call the vision API with modelId Little Java programming knowledge A fresh brain with a cup of coffee or tea 😃

Dataset: I have used Stanford’s open-source car image dataset. The car dataset content 16,185 images of 196 classes of cars.

Please follow step by step tutorial to integrate the vision model with Custom App.

Create a custom Object
Learning Shortlist: Object Detection
By: Aggregate Intellect
Tags: Object Detection, Computer Vision, Image Classification, Artificial Intelligence, Data Science

Resources

You should go through the following resources in the order that is provided:

CS231n: Convolutional Neural Networks for Visual Recognition

Type: Course; Theory, Main Concept

Estimated time commitment: 90 mins

Why is this a good resource: It is a Stanford university lecture on Detection and Segmentation that provides the basics you need

How to use this resource: Watch the whole video (optional: there are other related videos that you can watch too)

Instructor: Fei Fei Li

Link: http://cs231n.stanford.edu/

2. 12 Papers You Should Read to Understand Object Detection in the Deep Learning Era

Type: Blog post; Theory, Background Concept

Estimated time commitment: 90 mins

Why is this a good resource: This article has a comprehensive list of papers in object detection.

How to use this resource: Read the list of recommended papers

Author: Ethan Yanjia Li

Link: https://towardsdatascience.com/12-papers-you-should-read-to-understand-object-detection-in-the-deep-learning-era-3390d4a28891

3. Yolo-Models

Type: GitHub Repository; Implementation, Main Concept

Estimated time commitment: 90 mins

Why is this a good resource: Yolo-Model is one of the most famous models for the object detection task

How to use this resource: read the paper and play with the implementation

Creators: Joseph Redmon, Ali Farhadi

Link: https://github.com/eriklindernoren/PyTorch-YOLOv3

4. Image Recognition and Object Detection in Retail

Type: Blog Post; Application, Subsequent Concept

Estimated time commitment: 20 mins

Why is this a good resource: This post provides a concrete example of objection detection used in industry

How to use this resource: Read the post for 3 different use cases of object detection in industry

Authors: Ankit Singh, ParallelDots

Link: https://www.kdnuggets.com/2020/02/image-recognition-object-detection-retail.html
Make your first python ML model using pycharm.
By: Aryan Kaul
Tags: Python Programming, How To, Machine Learning Models, Image Classification, Machine Learning

The image above shows you how our final project will look like and identify Humans and Objects. So let's start step by step:

Step1: Downloading The libraries

A.Click Pycharm Then On preferences:

B. Adding the libraries; open your project interpreter and click on the + sign to add the libraries

Add and import these libraries in your code

Step 2: Use this class to load the dataset

The Code of this Class will be Given In the end

How to use this Class:

Use the class in this syntax to load your data

keep in mind that the path that you have given must contain both the folders of the object and the human image.

Step 3: Making The Machine Learning Model

This is the machine learning model that I made you can make your own if u want

Step 4: Prediction and Image

Use this code to predict and display the images

Where can you find the code and image dataset:

You can download the code from another article that I have made on my website. The code will be given at the end of the article in a doc format so you can copy-paste it and edit it to your convenience.

Download Dataset:

You will find free datasets for humans and object on my website so you can download it easily.

watch the above video for more clarity

Do you wanna learn how to make a mask detection app without any coding?

Then make sure that you check out this video:

Thank you for reading my article means a lot :)
Image classification from Amazon SageMaker to your phone with TensorFlowJS
By: Pierre Lucas
Tags: Mxnet, Amazon Sagemaker, Convert, Tensorflowjs, Image Classification

Amazon SageMaker offers built-in algorithms that allow you to train machine learning models quickly, hence enabling you to have a better time to market.

The built-in algorithms from Amazon SageMaker used for image classification are ResNets, as described in their documentation.

Once trained, these models are saved to the MXNet format. This format works well for many applications, but when it comes to load a model on a React Native mobile app developed with Expo, the number of possible formats narrows to one: TensorFlowJS.

This article describes the three steps to convert the Amazon SageMaker image classification built-in algorithm from MXNet to TensorFlowJS:

From MXNet to ONNX,

From ONNX to TensorFlow,

From TensorFlow to TensorFlowJS.

To leverage the simplicity of Amazon SageMaker, these steps should be performed on a Jupyter Notebook hosted on a ml.t2.medium instance.

Step 1 — From MXNet to ONNX

Amazon SageMaker saves the trained model in a S3 bucket as a model.tar.gz file. The latter contains two files describing the model: its architecture (.json) and its weights (.params). This is the MXNet format.

Unfortunately, Amazon SageMaker saves the model in an old version of MXNet, thus creating conversion issues if newer MXNet versions are used.

An easy workaround is to do the conversion on a Jupyter Notebook with a conda_mxnet_p27 kernel, which provides the right versions of MXNet and ONNX, respectively 1.6.0 and 1.3.0.

The following code converts your model from MXNet to ONNX.

Once the model is successfully converted to ONNX, you can proceed to the second stage.

Step 2 — From ONNX to TensorFlow

Alike the first conversion, the conversion from ONNX to TensorFlow requires specific versions of the libraries:

TensorFlow: 2.2.0,

ONNX: 1.7.0,

ONNX-TF: 1.6.0.

However, unlike the first conversion, this conversion should be done on a Jupyter Notebook with a conda_tensorflow2_p36 kernel.

The following code converts your model from ONNX to TensorFlow SavedModel format.

Once the model is successfully converted to the TensorFlow SavedModel format, you can proceed to the third stage.

Step 3 — From TensorFlow to TensorFlowJS

The last conversion should be done on the same kernel as the second conversion, namely conda_tensorflow2_p36.

The following code converts your model from TensorFlow to TensorFlowJS.

Once successfully completed, you should obtain a folder with:

A model.json file containing the model topology and a manifest of the weight files,

A set of sharded weight files saved in binary format.

These files are used to load the model on a mobile app with TensorFlowJS.

Conclusion

This article explained how to convert a built-in image classification model trained on Amazon SageMaker in three easy steps, from MXNet to TensorFlowJS, enabling an easy integration on a React Native mobile App developed with Expo.

An upcoming article will explain how to load this model and achieve inferences on an Expo App.
Microsoft Lobe: Machine Learning For All
By: Takashi Fujita
Tags: Microsoft Lobe, Image Classification, Machine Learning

A week ago, Microsoft released their first public preview of Lobe, a desktop app that promises to bring machine learning to the masses. In this first iteration of the software, you can train and test an image classifier without writing a single line of code.. so how well does it work?

I decided to give Lobe a go, and create a model to classify images of some of my daughter’s favorite animals: lions, cheetahs, zebras, and giraffes (she’s two-and-a-half and has mastered this exercise).

After installing Lobe, the first step was to drag-and-drop training images into the Lobe interface. I gathered the images (around 20 per label) from Google Images.

Importing training images into Lobe: just drag-and-drop

Next, I labeled the images (‘giraffe’, ‘lion’, etc). This could be done really quickly in batches by selecting multiple images at once:

Batch labeling is intuitive and quick

Lobe started training the model as soon as I started labeling images. Training was quick (1~2 minutes for around 100 images total).

After training was complete, Lobe allowed me to interactive with the trained model. I could drag-and-drop fresh images into the Play window and Lobe labeled them automatically, with very good accuracy!

Predictions by Lobe: so far so good!

From start to finish, the whole process (importing ~100 images, labeling, training, and testing) took me around 15 minutes.

Now of course, to use the models in real world applications (e.g. in apps, webpages, etc.), you’d need knowledge of the various model Export options available, including TensorFlow, Python, CoreML etc. But overall, I feel that Lobe provides a great way for anyone to build, experiment with, and optimize viable image classifier models.

Lobe Pros:

Completely free

An intuitive way for anyone to create ML models

Interface is clean and tidy, and user friendly

Official social communities (e.g. at Reddit) make issue sharing and resolution easy

Lobe Cons:
Salesforce Einstein Vision
By: Khadke Chetan
Tags: Salesforce Einstein, Image Classification, Machine Learning

In a commercial setting, the tasks to be delivered always comes to be completed in a short deadline. One of our clients has a very good reputation in the insurance sector. They always come with very creative ideas to help their client base and local repair store. In this new engagement, they had come with a new and unique requirement where they wanted to automatically tags uploaded images into a car and bike. Moreover, they also wanted to automatically locate dent in images to process with insurance. Although this looks like a very useful and interesting application, implementing and completing the integration in a short period of time is rather challenging. Moreover, the worrying part is that in my team no one had an extensive set of working experience with image data.

It is always challenging to work with Image data. Data science problems involving image data are often met with challenges in handling both volume and variety. In general working on problems related to computer vision, requires having a solid background in image processing fundamentals. These presumptions changed while I got to work with Salesforce Einstein's vision.

Einstein Vision provides different API that explores the power of deep learning models to classify and detect objects in the images at a larger scale. It also provides a pre-trained classifier or trains your custom classifier.

Photo by Chris Ried on Unsplash

Einstein Vision provides the following APIs:

Einstein Image Classification — API for developers to train models and classify images into different categories at scale

Einstein Object Detection — API for developers to train model and detect multiple objects within the Image

Let’s understand more about Einstein Vision Terminology

Terminology table

Please follow the following steps to get started with Salesforce Einstein.
SJTU’s MedMNIST Classification Decathlon: Lightweight AutoML Benchmarking for Medical Image Analysis
Tags: Computer Science, Computer Vision, Image Classification, Artificial Intelligence, Machine Learning

If there is one domain where professionals and the public are positively united on AI’s promise and potential, it may be medical image analysis, where SOTA computer vision (CV) techniques and image classification datasets are already outperforming human radiologists on tasks such as tumour and cancer detection. Now, a team of researchers from Shanghai Jiao Tong University (SJTU) has built a benchmark suite designed to help make these life-saving models even stronger.

Medical image analysis is the science of analyzing and processing biomedical images such as X-rays, CT scans and ultrasounds. While AutoML approaches continue to improve, the researchers note that there are currently few effective benchmarks for comparing these systems. To address the issue, they developed MedMNIST, a collection of 10 pre-processed medical image datasets released under Creative Commons (CC) licenses.

SJTU’s MedMNIST datasets are systematized to perform classification tasks on lightweight 28x28 images, which requires no clinical or CV background knowledge. They cover primary data modalities in medical image analysis and include diverse data scales (from 100 to 100,000).

The team designed the MedMNSIT Classification Decathlon to function as a benchmark for AutoML in medical image classification that can effectively demonstrate the performance of AutoML algorithms on all ten MedMNIST datasets without manual tuning.

The researchers say the datasets perform well in general with the Google AutoML Vision Method. They note however that typical statistical machine learning algorithms such as ResNet-18, ResNet-50, and autoKeras did not work well on the MedMNIST datasets.

The team says it hopes the MedMNIST Classification Decathlon benchmarks will help researchers speed up their medical image analysis algorithm studies.

The paper MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis is on arXiv. The MedMNIST datasets and code are on the project GitHub.
Car Classification using Inception-v3
By: Piyush Maheshwari
Tags: Deployment, Computer Vision, Image Classification, Deep Learning, Flask

Introduction

This article is about training 3 deep convolutional neural networks using Monk, which is an open source library for computer vision, and then deploying them through an API. The models take an image of a car as the input and then predict the Make, Model and Year of the car. The models have been trained on the Cars Dataset.

For transfer learning, the Inception-v3 architecture with pre-trained weights was used. Some initial layers were frozen and training was done on the remaining layers.

After training, the models were deployed through a Flask API. It accepts an image through a POST request and returns the predictions to the user.

For the training notebook, check this.

For the Flask API, check this.

Table of Contents
Reading: CBAM — Convolutional Block Attention Module (Image Classification)
By: Sik-Ho Tsang
Tags: Object Detection, Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning

The accuracy improvement of CBAM comes with a negligible parameter overhead, indicating that enhancement is not due to a naive capacity increment but because of the effective feature refinement.
[Paper] MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Image Classification)
By: Sik-Ho Tsang
Tags: Google, Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning

[Paper] MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (Image Classification)

In this story, MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (MorphNet), by Google AI, Google Brain, Energy-efficient multimedia systems group, MIT, and, Georgia Institute of Technology, is briefly presented. In this paper:

A simple and general technique for a resource-constrained optimization of DNN architectures which is adaptable to specific resource constraints (e.g. FLOPs), and capable of increasing the network’s performance.

which is adaptable to specific resource constraints (e.g. FLOPs), and capable of increasing the network’s performance. First, shrink an existing model such as ResNet using sparsifying regularizer. Then, expand all layers uniformly with width multiplier.

an existing model such as ResNet using sparsifying regularizer. Then, all layers uniformly with width multiplier. With the above shrink & expand process performed iteratively, the network is shrunken with improved performance.

This is a paper in 2018 CVPR with over 100 citations. (Sik-Ho Tsang @ Medium)
A Tutorial on Traffic Sign Classification using PyTorch
By: Suraj Krishnamurthy
Tags: Pytorch, Traffic Sign Recognition, Image Classification

Photo by Will Porada on Unsplash

Traffic Sign Recognition (TSR) is undoubtedly one of the most important problems in the field of driverless cars and advanced driver assistance systems (ADAS). TSR enables the front-facing smart cameras mounted on the car to recognize the signboards so that the car can act accordingly. Some examples include recognizing stop signs, speed limit, turn signs etc.

More formally, TSR is expected to perform two tasks:

1. Traffic Sign Detection: Detect all the signs from a given video frame

2. Traffic Sign Recognition: Recognize all the detected signs

The focus of this blogpost is to introduce the second step alone i.e., the recognition part. I assume that the signboards are all detected and the sign part alone is cropped and stored as a separate image. However, detection and classification can be solved as a single problem, which will be addressed in a separate blogpost.
Reading: BAM — Bottleneck Attention Module (Image Classification)
By: Sik-Ho Tsang
Tags: Object Detection, Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning

Reading: BAM — Bottleneck Attention Module (Image Classification)

In this story, BAM: Bottleneck Attention Module (BAM), by, Korea Advanced Institute of Science and Technology (KAIST), and Adobe Research, is presented. In this story:

A new module, Bottleneck Attention Module (BAM) , is designed, that can be integrated with any feed-forward CNNs.

, is designed, that This module infers an attention map along two separate pathways, channel and spatial .

. It is placed at each bottleneck of models where the downsampling of feature maps occurs.

This is a paper in 2018 BMVC with over 100 citations. (Sik-Ho Tsang @ Medium)
Clasificación de imágenes con mapas autorganizados.
Tags: Image Classification, Rstudio, Clustering, Neural Networks, Self Organizing Map

desareca/SOM-MINST-Clasification

You can’t perform that action at this time. You signed in with another tab or window. You signed out in another tab or…
Reading: ESPNetv2 — A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network (Image Classification, Semantic Segmentation, etc)
By: Sik-Ho Tsang
Tags: Convolutional Network, Semantic Segmentation, Artificial Intelligence, Image Classification, Deep Learning

Reading: ESPNetv2 — A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network (Image Classification, Semantic Segmentation, etc)

In this story, “ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network” (ESPNetv2), by University of Washington, Allen Institute for AI (AI2), and XNOR.AI, is presented. In this paper:

EESP: Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions are proposed to learn representations from a large effective receptive field with fewer FLOPs and parameters.

It can be used in four tasks: (1) image/multi-object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling.

In particular, ESPNetv2 outperforms ESPNet by 4–5% and has 2−4× fewer FLOPs on the PASCAL VOC and the Cityscapes dataset.

This is a paper in 2019 CVPR with over 70 citations. (Sik-Ho Tsang @ Medium)
Reading: ShuffleNet V2 — Practical Guidelines for Efficient CNN Architecture Design (Image Classification)
By: Sik-Ho Tsang
Tags: Object Detection, Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning

Reading: ShuffleNet V2 — Practical Guidelines for Efficient CNN Architecture Design (Image Classification)

In this story, “ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design” (ShuffleNet V2), by Megvii Inc (Face++), and Tsinghua University, is presented.

Indirect Metric : Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs .

: Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., . Direct Metric: e.g., speed, depends on the other factors such as memory access cost (MAC) and platform (GPU or ARM) characteristics.

In this paper:

Practical guidelines are suggested for efficient network design using MAC and experimental observations using GPU/ARM.

using MAC and experimental observations using GPU/ARM. ShuffleNet V2 is proposed according to the practical guidelines, which obtains high accuracy with also high speed as shown above.

This is a paper in 2018 ECCV with over 700 citations. (Sik-Ho Tsang @ Medium)
Reading: ENAS — Efficient Neural Architecture Search via Parameter Sharing (Image Classification)
By: Sik-Ho Tsang
Tags: Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning, Neuralarchitecturesearch

Reading: ENAS — Efficient Neural Architecture Search via Parameter Sharing (Image Classification)

In this story, Efficient Neural Architecture Search via Parameter Sharing (ENAS), by Google Brain, Carnegie Mellon University, and Stanford University, is presented. In this paper:

ENAS constructs a large computational graph, where each subgraph represents a neural network architecture , hence forcing all architectures to share their parameters.

, hence forcing all architectures to share their parameters. Sharing parameters among child models allows ENAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches.

This is a paper in 2018 ICML with over 800 citations. (Sik-Ho Tsang @ Medium)
Reading: Cutout — Improved Regularization of Convolutional Neural Networks (Image Classification)
By: Sik-Ho Tsang
Tags: Convolutional Network, Artificial Intelligence, Image Classification, Regularization, Deep Learning

Reading: Cutout — Improved Regularization of Convolutional Neural Networks (Image Classification)

Cutout applied to images from the CIFAR-10 dataset.

In this story, Improved Regularization of Convolutional Neural Networks with Cutout (Cutout), by University of Guelph, and Canadian Institute for Advanced Research and Vector Institute, is shortly presented.

Due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well.

and therefore require proper regularization in order to generalize well. In this paper, simple regularization technique of randomly masking out square regions of input during training is proposed, which is called cutout, can be used to improve the robustness and overall performance.

This is a paper in 2017 arXiv with over 500 citations. (Sik-Ho Tsang @ Medium)
Reading: SqueezeNext — Hardware-Aware Neural Network Design (Image Classification)
By: Sik-Ho Tsang
Tags: Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning, Data Science

1. SqueezeNext (SqNxt) Block

Illustration of a ResNet block on the left, a SqueezeNet block in the middle, and a SqueezeNext (SqNxt) block on the right.

Suppose Ci and Co are the input and output channel sizes respectively. Filter size is K².

The total number of parameters in this layer will then be K²CiCo. Essentially, the filters would consist of Co tensors of size K×K×Ci.

1.1. Parameter Reduction at Filter Size

The first change that authors make, is to decompose the K×K convolutions into two separable convolutions of size 1×K and K×1. This effectively reduces the number of parameters from K² to 2K, and also increases the depth of the network, as shown at the right of the above figure. (This is also the factorization proposed in Inception-v3.)

These two convolutions both contain a ReLu activation as well as a batch norm layer (BN-Inception / Inception-v2).

1.2. Parameter Reduction at Channel Number

Another factor is the multiplicative factor of Ci and Co significantly increases the number of parameters in each convolution layer.

One idea would be to use depth-wise separable convolution, suggested in MobileNetV1, to reduce this multiplicative factor, but this approach does not good performance on some embedded systems due to its low arithmetic intensity (ratio of compute to bandwidth).

Another ideas is the one used in the SqueezeNet architecture, where the authors used a squeeze layer before the 3×3 convolution to reduce the number of input channels to it.

Here, authors use a variation of the latter approach by using a two stage squeeze layer, as shown at the right of the above figure.

In each SqueezeNext block, two bottleneck modules are used, each reducing the channel size by a factor of 2, which is followed by two separable convolutions. A final 1×1 expansion module is used, which further reduces the number of output channels for the separable convolutions.
Pytorch Image Segmentation Tutorial For Beginners — II
By: Seyma Tas
Tags: Pytorch, Image Classification, Image Segmentation, Deep Learning, Machine Learning

Pytorch Image Segmentation Tutorial For Beginners — II

In the previous tutorial, we prepared data for training. We downloaded the dataset, loaded the images, split the data, defined model structure, downloaded weights, defined training parameters.

In the second part of the tutorial, we train the model and evaluate the results of the model. I

Prepare Training

We first define the device and switch between CPU and GPU when needed.

device = torch.device(‘cuda’) if torch.cuda.is_available() else torch.device(‘cpu’)

We load the model to the device.

model.to(device)

We initialize two lists to store loss values.

loss_history = [] loss_history_val = []

We define best_loss_val, and make it store an infinite value. Thus, best_loss_value acts as an unbounded upper value for comparison. This is useful for finding the lowest value for the loss.

best_loss_val = float(‘inf’)

Train

We start training.

print(“Start train…”)

We make a for loop for every epoch.

We set the train mode.

for epoch in range(50): #Train mode model.train() loss_running = [] for _, (x,y) in enumerate(train_dataloader): x, y = x.float().to(device), y.float().to(device) pred = model(x) loss = criterion(pred, y) loss_running.append(loss.item()) optimizer.zero_grad() loss.backward() optimizer.step() loss_history.append(np.mean(loss_running))

We set the evaluation mode.

# Evaluate mode model.eval() with torch.no_grad(): loss_val_running = [] for _, (x_val, y_val) in enumerate(val_dataloader): x_val, y_val = x_val.to(device), y_val.to(device)

We do predictions, calculate loss, append the calculated loss to the list of train and validation losses. (loss_running, loss_val_running)

pred_val = model.forward(x_val) #pred_val = model(x_val) loss_val= criterion(pred_val, y_val) loss_val_running.append(loss_val.item()) curr_loss_val = np.mean(loss_val_running) loss_history_val.append(curr_loss_val)

We save the best weights of the model for later use.

# Save the best weights if curr_loss_val < best_loss_val: best_loss_val = curr_loss_val torch.save(model.state_dict(), ‘/content/drive/My Drive/Brain-Tumor-Segmentation-Project/best_model.pth’)

We change the learning rate by a learning rate scheduler we defined before.

# Change the learning rate scheduler.step()

We print the results.

# Print the results print(“epoch”, epoch, “train loss”, loss_history[-1], “val loss”, loss_history_val[-1])

Here is the block of code.

We load the best model’s weights and evaluate the model.

checkpoint = torch.load('/content/drive/My Drive/Brain-Tumor-Segmentation-Project/best_model.pth', map_location=torch.device('cpu')) model.load_state_dict(checkpoint)

Let’s now see what we did.

def plot_mask(mask_3d_array, axx): # takes 64*64*64 array mask_cpu = mask_3d_array.cpu().detach().numpy() reshaped_mask_cpu = np.reshape(mask_cpu,(512, 512)) print(np.max(reshaped_mask_cpu), np.min(reshaped_mask_cpu)) reshaped_mask_cpu_bin = np.round(reshaped_mask_cpu) axx.imshow(reshaped_mask_cpu_bin)

Plot some predicted and ground truth masks
Exploratory Data Analysis Ideas for Image Classification
By: Eunjoo Byeon
Tags: Data Science, Image Classification, Data Visualization, Python, Data Analysis

Exploratory Data Analysis Ideas for Image Classification

Photo by CDC on Unsplash

Exploratory data analysis comprises of brief analyses to describe a dataset to guide the modeling process and to answer preliminary questions. For classification problems, this might include looking at the distributions of variables or checking for any meaningful patterns of predictors across different classes. The same problem holds for the classification of image data. We intend to find meaningful information simple operations can give us. Here, I outline a couple of methods we can do to achieve this goal using Chest X-Rays data [source]. This dataset consists of X-ray images of pneumonia patients and healthy controls.

Raw Comparison

First, we can start by simply looking at a few randomly sampled images.

This step will pull random images from each sub-folders and display them.

Images as Matrix

For the next few steps, we will work directly with the pixel values of each image so we can do operations on them. We can accomplish this by converting our images into a Numpy array.

This function will iterate through each file and turn them into an (n, m) matrix, where n is the number of observations and m is the number of pixels.

Average Image

Now let’s see what the average image looks like for each class. To compute the average image, we can take the average value of each pixel across all observations.

We can see from the average image that pneumonia X-rays tend to show higher obstruction around the chest area.

Contrast Between Average Images

Using the average images, we can also compute the difference.

Variability

Similarly, we can also look at which area is most variable in either class by computing variance or standard deviation instead of the mean. Here the lighter area indicates higher variability. Again we can see that in pneumonia X-rays, there is more variability within the lungs.

Eigenimages

Lastly, we can use a dimension reduction technique such as the principal component analysis (PCA) to visualize the components that describe each class the best. The eigenimages, which is essentially the eigenvectors (components) of PCA of our image matrix, can be reshaped into a matrix and be plotted. It’s also called eigenfaces as this approach was first used for facial recognition research. Here we will visualize the principal components that describe 70% of variability for each class.
지루하지 않게 코노미 사진 4만장 어노테이션하기 (머신러닝과 함께)
By: Doyoung Gwak
Tags: Semi Supervised Learning, Dataset, Image Classification, Annotations, Machine Learning

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Getting started on GCP AutoML Vision
By: Sadhana Paladugu
Tags: Dog Breed Classifier, Image Classification, Computer Vision, Machine Learning

Want to benchmark your image classification model with data you have before even writing code?

I was developing a model to do image classification of dog breeds and ran into few questions like how good is my data, what ratio to split it for train and test sets, and few other hyperparameters to decide on. So, I have chosen the GCP AutoML Vision cloud service which does end-end pipeline process from pre-processing the data to deploying the model. Below are the steps to follow to benchmark your model using GCP AutoML Vision

Step1: Data Cleaning

Since the data that I gathered through AWS is already in a structured format(Class of images in a folder and folder name as the class name of images), I need not have to do much of the data preparation steps. However, to match GCP’s upload validations for the image, I had to do the following to my data

Remove the period from all file names

Limit file names to be only 32 characters

You can just upload the data with minimum structure and GCP tells you if you have to clean it.

Step2: Go to https://console.cloud.google.com/ and from the menu bar navigate to Artificial Intelligence ->Vision

Step3: Import images into GCP cloud

Create a dataset and give it a name. From the import tab, upload all the data containing images and choose a destination to google cloud storage data bucket. If there is no existing one, create one using the option available.

I have around 6500 images and the step took around 2–3 hours.

We get an email notification if the images are successfully uploaded or if there are any errors in our data format.

Step4: Verify your data

After successful upload, go to the IMAGES tab to see if the images are correctly imported as uploaded.

Here we can select any images that are not labeled and can assign them one of the listed labels using the “Assign Labels” option as shown below.

GCP automatically divides our dataset into Train, Test, and Valid datasets

We can see the stats using the “LABEL STATS” option available. For my dataset, it divided into 80% training data,10% valid data, and 10% test data.

STEP4: Train your data

After successful data upload, click on the TRAIN tab to train the data.

The training process took me around 6–7 hours.

Results:

The model completed the training part successfully and it gave me a 97% accuracy score with test data (that the GCP divided by itself) having precision 93.43% and recall 90.39% with a confidence threshold of 0.5

We can adjust the confidence threshold to see how precision and recall vary.

Below is the confusion matric obtained.

Some findings from the above matrix

The model can categorize dogs of the Greyhound class with 100% accuracy whereas it got confused with the Entlebucher mountain dog breed and Greater swiss mountain dog breed.
Quickly Create an Image Dataset for Classification
By: Jonathan Dimuccio
Tags: Dataset, Image Classification, Data Science, Machine Learning

It’s a very common occurrence to know what item you want to classify — but not have a clear set of images for this classification. In this quick tutorial I will go over how to curate a set of images that can be used to train a classifier.

Bing Image Downloader

The most popular image search downloader is google image downloader; so you may be wondering why I am not using it. The reason is: there have been many issues that have arisen in the past few months, and it is pretty much broken.

The main star in this show will be this bing image downloader, which is a fork of the original script. This was created as a solution to the many recent issues that have come up with google image downloaders. As someone who is new to image classification, this was a very exciting find. With just a few lines of code, we can curate thousands of images from a specific bing image search.

1. Install using git clone

The downloader is easily installed with a few terminal lines:



cd google-images-download

pip install -U -r requirements.txt git clone https://github.com/ultralytics/google-images-download cd google-images-downloadpip install -U -r requirements.txt

2. Download Chrome / ChromeDriver

Chances are you already have chrome, but if not, here is the download page. You’ll also need chromedriver to access the bing images through the terminal. The lastest stable release is what worked for me (on MacOS), which is currently version 85.

If the version of chromedriver dosen’t match your installation, there will be an error output that will alert you of the currently downloaded version of chrome. Then, troubleshoot by redownloading the chromedriver version that matches your Google Chrome version.

3. Curate some images

Let’s say I wanted picture of some cute puppies. All it takes is:

cd google-images-download

python3 bing_scraper.py --url 'https://www.bing.com/images/search?q=puppies' --limit 10 --download --chromedriver /Users/a1425/Documents/chromedriver

A new folder will be created in the current destination, storing the desired number of images.

4. Start Classifying!

This is just the beginning step of creating an image classification system, but it’s a great start nonetheless. With just a few terminal commands, thousands of images can be downloaded from bing to a local directory.
Create an Image Classifier with OpenCV and Jupyter Lab | TensorFlow image Classifier
Tags: Image Processing, TensorFlow, Computer Vision, Image Classification, Opencv

In this video on creating an image classifier with Opencv and Jupyter Lab | TensorFlow image classifier, we are going to set up an ipynb with anaconda, install the necessary packages and run an IPYNB which will create identify objects in pictures.

Setup a Live Sketch Filter with Opencv and Jupyter Lab: https://www.youtube.com/watch?v=DqefP...

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Introduction to Image Processing
By: Phil S
Tags: Python, Image Classification, Image Processing, Data Science

Hello,

This is my first story here, so be easy on me ;)

I am very interested in image processing. The background is as follows: I would like to learn more about autonomous driving, specifically how computer vision and image processing works. I found an open source dataset (https://challenge2020.isic-archive.com/) consisting of “33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. Each image is associated with one of these individuals using a unique patient identifier.” This was part of a kaggle challenge which has since ended, but I learned a lot from it and thought it is a good intro to share with those interested.

In short, we have image data and the goal is to manipulate the data into a “machine readable” format — preferably in a dataframe — to run predictions.

First, we need to explore the images, and here we are faced with multiple challenges. The pictures come in different sizes and quality.

Sample Image

Thus, I needed to scale them to a uniform size. I recognize that this distorts some images and there are potentially more advanced ways, but the images do not only come in different rectangle or square shapes, but also circles.

Images come in different shapes

I decided to move forward with this approach given the amount of pictures and this solution being sufficient — both in terms of quality as well as processing time.

Resizing Images

To make sure, it actually worked, we can check our images:

Displaying Image

Afterwards, we have multiple choices how to move forward:

Saving each pixel in RGB format (so for each pixel we have 3 integers)

Saving each pixel in greyscale (so for each pixel we have 1 integer)

I went with the latter, as this results in 400 x 400 features. This is already a lot to handle locally with limited processing, and the reason I use PCA for the predictions, but this is another story.

Comverting Images to Grayscale

Then, as previously described, we convert the now grayscaled, uniform sized images into arrays, and flatten them.

Converting images to machine readable data, arrays in that case

So our result is a dataframe consisting of 160,000 features and we can read in the image names, as these are patient IDs in the dataset.

Retrieving title of Image and storing in column

Next, we merge the two datasets; the one we created and the one provided to us, to get the binary target into one dataset.

Finally, we can run predictions with the machine readable data format. I tried logistic regression, which only predicts the majority class, so I upsampled and proceeded with PCA, xgboost and neural nets. This is not the main topic of this short image processing intro but maybe I can revisit it in another post.

Hope this helps aspiring “image processors” ;)
Reading: PNASNet — Progressive Neural Architecture Search (Image Classification)
By: Sik-Ho Tsang
Tags: Convolutional Network, Artificial Intelligence, Image Classification, Deep Learning, Neuralarchitecturesearch

Reading: PNASNet — Progressive Neural Architecture Search (Image Classification)

In this story, Progressive Neural Architecture Search (PNASNet), by Johns Hopkins University, Google AI, and Stanford University, is presented. In this paper:

A sequential model-based optimization (SMBO) strategy is used such that a surrogate model is learnt to guide the search through structure space .

is used such that . Direct comparison under the same search space shows that PNASNet is up to 5 times more efficient than the Reinforcement Learning (RL) method, 8 times faster in terms of total compute.

This is a paper in 2018 ECCV with over 700 citations. (Sik-Ho Tsang @ Medium)
Train MNIST Dataset on LeNet Model
By: Tanuj Shrivastava
Tags: Mnist, Convolutional Network, Image Classification, Deep Learning, 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
Reading: AmoebaNet — Regularized Evolution for Image Classifier Architecture Search (Image Classification)
By: Sik-Ho Tsang
Tags: Convolutional Network, Evolutionary Algorithms, Image Classification, Artificial Intelligence, Deep Learning

This favors the newer models in the population. aging evolution allows us to explore the search space more, instead of zooming in on good models too early.
How Convolution Neural Networks work —with Image Classification Keras Code
By: Balavivek Sivanantham
Tags: Convolutional Network, Keras, Image Classification, Deep Learning, Python

In this blog, we will be discussing a convolutional neural network with an example implementation on python. The convolutional neural network is also called as ConvNets or CNN, It is an artificial network so far mostly used on image analysis. Image analysis is where CNN’s are widely used but CNN can be used in other classification problems as well. Most generally we can think of CNN as an Artificial Neural Network that has some type of specialization to be able to pick out or detect patterns.

This pattern makes CNN so useful for image analysis. CNN is some form of an artificial neural network, so what differentiates it from a standard multi-layer perceptron(MLP). CNN has hidden layers called the convolutional layer, these layers make a CNN.

What is a Convolutional Layer?

A Convolutional layer just like any other layers a convolutional layer receives input then transforms input in some form and outputs the transformed input to the next layer with a convolutional layer this transformation is called convolutional operation. These layers play a major role in detecting patterns. In each convolutional layer, we need to specify the number of filters the layers should have.

Filters are used by the convolutional layer to precisely identify patterns. Let's consider an image using MINST data, In this image, there are multiple edges, shapes, textures objects, etc. So one filter will be detecting edges in the image that would be called an edge detector. Similarly, there could be different filters to detect corners, some may detect circle, etc. Deeper the neural network goes the network might be able to detect more shapes like eyes, ears, even face, etc.