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 takes inputs from previous nodes — adjusted with unique biases and weights (also coming from previous nodes), then do some calculations (and measurements) to produce output to solve a problem by approximation. Machine learning offers the ability to extract certain knowledge and patterns from a series of observations. Follow-on articles have more discussions on the topic (2018–2019), and it will continue to do so for a few more years to come as the field is still exciting with many new developments and breakthroughs. Andi Sama et al., 2019b, “Think like a Data Scientist”. Stage-1 and stage-2 are basically development-stage while stage-3 is runtime-stage. Andrew Widjaya, Cahyati S. Sangaji, 2019, “Face Recognition, Powered by IBM Cloud, Watson & IoT on Edge”, SWG Insight, Edisi Q2 2019. 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 result of lr_find() shows that we are suggested to set our learning rate range between 3x10–4 to 3x10–3 (the stable value range in the graph just before it is going up). deep learning-based image segmentation models. And then came Deep Learning, and it changed everything once and for all, and many different architectures have been experimented since then. Illustration-7 is visualizing images in CamVid database along with its valid labels. So we use train_test_split two times. touching on the peripherals of these topics - Image classification is a class of problem wherein we are concerned with the presence of an image in a scene, followed by that is the Image detection and Localisation which determines the region where a give objects are located and drawing a boundary box/ellipse around them, however, there is big brother to them which is. Providing the right resource & skill set (data scientist and computing power), modeling should be a straightforward task, e.g. 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. We review on how we are doing so far (illustration-11). deep learning technology into the diagnosis of burns. Illustration-2 shows a brief overview on the evolution and advancements in AI since 1950s. [5]Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics [6]Exploring Uncertainty Measures in DeepNetworks for Multiple Sclerosis Lesion Detection and Segmentation [7]Deep Bayesian Active Learning with Image Data Similarly, we can also use image segmentation to segment drivable lanes and areas on a road for vehicles. Recently, the 3rd category emerges: Reinforcement Learning (action-based learning based on certain defined rewards). These functional layers often contains convolutional layers, pooling layers and/or fully-connected layers. Andi Sama et al., 2019a, “Image Classification & Object Detection”. This article “Image Segmentation with Deep Learning, enabled by fast.ai framework: A Cognitive use-case, Semantic Segmentation based on CamVid dataset” discusses Image Segmentation — a subset implementation in computer vision with deep learning that is an extended enhancement of object detection in images in a more granular level. It seems that we can still improve our model to be better. 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. Kaggle is a recognized place for competing for the best in the world in the area of deep learning by continuing to improve and invent the better algorithms (with million dollars reward for selected world-class’s tough challenges). The size of data to be processed is set at 50% of the total src_size. We designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network (Mask R-CNN). In these the input goes to CNN and output layer has dimensions equal to the number of classes that are required i.e Score map, the output is up-sampled by interpolation, without any parameters unlike convolution net, which then goes through fully connected CRFs(Conditional Random Fields) for post-processing, CRFs are probabilistic model which are good at predicting probabilities of given set from a different given set of probabilities and thus the model performs some approximation, which provides quite accurate results without affecting the feasibility in the process. 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. We review on how are we doing so far (illustration-10). We call a fast.ai’s function to find a learning rate to start with as in illustration-9.b. U-Net (U-net: Convolutional networks for biomedical image segmentation) SegNet (Segnet: A deep convolutional encoder-decoder architecture for image segmentation) PSPNet (Pyramid scene parsing network) GCN (Large Kernel Matters) DUC, HDC (understanding convolution for semantic segmentation) Mask-RCNN (paper, code from FAIR, code PyTorch) Fig. Based on the result of lr_find(), we decide to set the learning rate to 1x10–3 (illustration-15). Well, it was mentioned before that each pixel of a segmented image contains class information in either one of 32 defined classes ‘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’ — along with its probabilities. 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 modeling that can take just a few minutes on GPU, can take hours if using CPU. An Obligatory request, If you are happy with what you know, Clap your hands.! … to deploy in web or mobile apps. 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