A Cognitive use-case, Semantic Segmentation based on CamVid dataset. Then, we can start training the dataset (modeling), in this case for Semantic Image Segmentation. Arfika Nurhudatiana, Ph.D — an AI-Practitioner who has a Data Scientist role and based in Jakarta, Indonesia emphasizes on this “Deep learning extends machine learning by excluding manual feature extraction and directly learns from raw input data.”, Commenting further on Image Classification & Image Segmentation, she continues “One of the reasons for the rising popularity of R-CNN-based (Region-based Convolutional Neural Network) approach for object detection is due to its sweet combination of image segmentation and image classification. Image Segmentation in Machine Learning Various image segmentation algorithms are used to split and group a certain set of pixels together from the image. Years of research have been devoted to this, and many new advanced developments have emerged and keep coming in the last few years, especially in computer vision through invention of new algorithms & new optimization methods. It uses a lot of data to teach the machine to enable machine to do things that human can do, see things and be able to recognize objects for example. How image matting works with segmentation. Those images can be manually edited to remove unwanted files. It can be about 10 times slower. After the ROI pooling, we add 2 more convolution layers to build the mask. You might have wondered, how fast and efficiently our brain is trained to identify and classify what our eyes perceive. [43] adopt the standard CNN as a patchwise pixel classifier to segment the neuronal membranes (EM) of electron microscopy images. to deploy in web or mobile apps. The limited set of multi-threads within one virtual machine or within one container is meant to prevent the system’s resources (CPU, RAM, GPU) to be exhausted within that virtualized environment. Illustration-22 shows a typical AI data pipeline, where data flows through 3-stages: 1. data preparation, 2. modeling as well 3. deployment/inferencing. This is the power of parallel processing embedded in GPU for processing complex computations, that consists mostly of matrix operations (matrix multiplications & additions as in linear algebra) as well as 1st degree partial differential processing in back-propagation algorithm. Lets now talk about 3 model architectures that do semantic segmentation. Training (Initial, with the Part of Dataset). The label file consists of index values that act like pointers, referring to each pixel in the segmented image. We designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network (Mask R-CNN). Illustration-12 shows the paired result, ground truth (images from our data) and predicted images (the result of prediction with our current model at this stage). It is a technique of dividing an image into different parts, called segments. The 2015 ImageNet’s result has surpassed human expert that could achieve it at only 5.1%. In recent years, the success of deep learning techniques has tremendously influenced a wide range of computer vision areas, and the modern approaches of image segmentation based on deep learning are becoming prevalent. This article is a comprehensive overview including a step-by-step guide to implement a deep learning image segmentation model. Fig. Ilustration-9a shows the python code within Jupyter Notebook, in which we define acc_camvid() function to calculate accuracy for our model and prepare the base model for training by calling unet_learner() fast.ai’s function then assign it to the object called learn (using Convolutional Neural Network (CNN)-based neural network architecture called Resnet-34). Deep Conversation neural networks are one deep learning method that gives very good accuracy for image segmentation. Now let’s learn about Image Segmentation by digging deeper into it. Note that, default learning rate in fast.ai has been set to 0.003 (3x10–3), and in this case we can run fit_one_cycle() function for a few epochs before using lr_find(). The companion article “Image Classification with Deep Learning, enabled by fast.ai framework: A Cognitive use-case, 4-classes Image Classification” discusses Image Classification. Human can naturally sense the surrounding areas through various biological sensors such as eye for vision, ear for hearing, nose for smelling, as well as skin for sensing. Deep learning has become the mainstream of medical image segmentation methods [37–42]. And then came Deep Learning, and it changed everything once and for all, and many different architectures have been experimented since then. The notable breakthrough of advancement in the field of computer vision using deep learning was in 2012 when an applied algorithm called Convolutional Neural Network (a.k.a. Illustration-7 is visualizing images in CamVid database along with its valid labels. A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum. In deep learning, we need to make 3 splits: Train, test, and validation. It is an image processing approach that allows us to separate objects and textures in images. The original network won the ISBI cell tracking challenge 2015, by a large margin, and became since the state-of-the-art deep learning tool for image segmentation. Subsequent results in 2013, 2014 and 2015 were at 11.7%, 6.7%, and 3.57% respectively. Pieter Abbeel, 2019, “Full Stack Deep Learning — Lecture 10: Research Directions”, Deep Learning Bootcamp, March 2019, Berkeley. Once a model has been created, deployment should be “easier” to implement — e.g. In short, they are the external variables that are set before the training to generate optimized dependent variables in neural network structure “model”: namely weights & biases. It is suggested then, to save the generated file (model) for each epoch (the saved model contains learned weights and network architecture for all connected layers in the model). Once the base model for training is defined, we can start the training (illustration 9-c) by calling fast.ai’s fit_one_cycle() function with hyperparameters: 10, lr and 0.9. We use training dataset from CamVid database (Brostow, Shotton, Fauqueur, Cipolla, 2008a) and test dataset from Google Images Search (manually generated). At the other end, the application logic “subscribes to the request_message topic”, so it will receive the data as soon as the data arrives to be passed to inference engine (after data has been decompressed). It discusses a use-case in processing CamVid dataset to train a model for Semantic Image Segmentation to recognize each pixel in the image, that is belong to either one of 32-classes (categories), by using fast.ai libraries. Adoption for Machine Learning (ML) is accelerating rapidly especially with the availability of cloud-based platform to experiment (with GPU). Once everything is setup, we can start using Jupyter Notebook to enter our python code to experience deep learning by pointing our browser to http://localhost:8080/tree/ then navigate to a directory where our .ipynb file resides (as in illustration-5). A more granular level of Image Segmentation is Instance Segmentation in which if there are multiple persons in an image, we will be able to differentiate person-1, person-2, person-3 for example along with other objects such car-1, car-2 and tree-1, tree-2, tree-3, tree-4 and so on. We review on how we are doing so far (illustration-11). The training and validation can be repeated several times to improve the accuracy, although at some point the accuracy may be decreased. Training (and Validation) Dataset from CamVid database. Take a look, An Introduction to TensorFlow and implementing a simple Linear Regression Model, Ad2Vec: Similar Listings Recommender for Marketplaces, Autoencoders and Variational Autoencoders in Computer Vision, Deep Learning for Image Classification — Creating CNN From Scratch Using Pytorch, Introduction To Gradient Boosting Classification, Brief Introduction to Model Drift in Machine Learning. It is an image processing approach that allows us to separate objects and textures in images. AI, including inferencing can be part of a large business process such as Business Process Management (BPM) within an Enterprise AI or run as a server process accessed by external applications like mobile app or web-based app or even accessed by a subprocess within an external application somewhere within multi-clouds or hybrid cloud environment. The objective of this project is to label pixels corresponding to road in images using a Fully Convolutional Network (FCN). Andi Sama et al., 2018, “Deep Learning — Image Classification, Cats & Dogs — A Cognitive use-case: Implement a Supervised Learning for Image Classification”, SWG Insight, Edisi Q1 2018. Deep learning is a type of machine learning that is so happening in recent years. Well, with Artificial Intelligence (AI) and especially Deep Learning, this is becoming more possible in recent years. Which can help applications to … In fast.ai, there is a function called lr_find() to find a range of possible learning rate values that are suitable for minimizing our error_rate. https://medium.com/.../deep-learning-for-image-segmentation-d10d19131113 Andi Sama et al., 2019b, “Think like a Data Scientist”. We save our current generated result at this stage, and just call it as “stage-2”. self driving car) for instance. Machine Learning is a subset of AI. Well, maybe we can improve more by pushing our last accuracy 87.04% to be better. A model is an approximation on the relationship between input and output, based on dataset. The Cambridge-driving Labeled Video Database (CamVid) is the first collection of videos with object class semantic labels, complete with metadata. 3 min read. In… We review on how are we doing so far (illustration-10). The set of application logic + inference engine may also be configured as multi-threads in which it can handle multiple requests and perform multiple inferences in one pass within a process. We observe that, with all the base hyperparameters set (such as learning rate & measurement metrics), for the first 10 epochs: 1st (epoch 0), 3rd, 5th, 7th,8th , 9th and 10th, we get 82.81%, 83.30%, 86.97%, 86.40%, 89.04%, 85.54% and 87.04% accuracies (acc_camvid()) respectively. We observe that, by referring to all 10 epochs: 1st (epoch 0), 2nd, 3rd, 8th , 9th and 10th, we get 91.91%, 92.47%, 91.09%, 91.72%, 92.21%, and 92.21% accuracies respectively. An intelligent robot that can navigate the environment for example, avoiding obstacles while walking around and going through the path carefully without explicitly programmed towards achieving just one goal to arrive in a predefined destination — and these all should be with special safety caution: not to harm any living things like human and animal. Illustration-2 shows a brief overview on the evolution and advancements in AI since 1950s. In an enterprise-level configuration such as with IBM POWER AC922 server, we can enable even more scalable multiple servers with multiple GPUs configuration to significantly speed up the modeling. The modeling will produce a model, such that when given an image, it can predict an expected segmentation (output) within a certain confidence level. A lot of segmentation algorithms have been proposed for addressing specific problems. For extracting actual leaf pixels, we perform image segmentation using K-means… The database provides ground truth labels that associate each pixel with one of 32 semantic classes. Figure 13. As with image classification, convolutional neural networks (CNN) have had enormous success on segmentation problems. Ever wonder how does an intelligent machine see the world? The advancements of high-speed hardware and availability of bigdata, have been accelerating this area of study with successful selected implementations in the real world with many more potential practical applications in the future. deep learning technology into the diagnosis of burns. Modeling using training and validation data with a full dataset would typically require a great amount of time, meaning more GPU time to spend. Inferencing can be done either on-premise or on-cloud or in combination, it is just deployment options that we need to select considering reliability and scalability that fit to the purpose of deployment (of course, cost factor is also one of the important factors to consider here). Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. Fei-Fei Li, Justin Johnson, Serena Yeung, 2017, “CS231n: Convolutional Neural Networks for Visual Recognition”, Stanford University, Spring 2017. Note that although you can use CPU-only, the training time will be significantly slower. Download Data. We then write a custom pyhton function to extract this information (as shown in Illustration-19a, illustration-19b). It is worth to study it to know the development of deep-learning-based instance segmentation.Sik-Ho Tsang The method segments 3D brain MR images into different tissues using fully convolutional network (FCN) and transfer learning. And we are going to see if our model is able to segment certain portion from the image. — This is like the tool that everyone working on computer vision first runs to. We can use “publish to a topic, e.g. Our brain is able to analyze, in a matter of milliseconds, what kind of vehicle (car, bus, truck, auto, etc.) Andi Sama et.al, 2019c, “Guest Lecturing on AI: Challenges & Opportunity”, Lecture to FEBUI — University of Indonesia”. These functional layers often contains convolutional layers, pooling layers and/or fully-connected layers. Are we satisfied? The 32-classes are defined as ‘Animal’, ‘Archway’, ‘Bicyclist’, ‘Bridge’, ‘Building’, ‘Car’, ‘CartLuggagePram’, ‘Child’, ‘Column_Pole’, ‘Fence’, ‘LaneMkgsDriv’, ‘LaneMkgsNonDriv’, ‘Misc_Text’, ‘MotorcycleScooter’, ‘OtherMoving’, ‘ParkingBlock’, ‘Pedestrian’, ‘Road’, ‘RoadShoulder’, ‘Sidewalk’, ‘SignSymbol’, ‘Sky’, ‘SUVPickupTruck’, ‘TrafficCone’, ‘TrafficLight’, ‘Train’, ‘Tree’, ‘Truck_Bus’, ‘Tunnel’, ‘VegetationMisc’, ‘Void’, and ‘Wall’.

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