- Keras Mask R-CNN
- A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
- Improving the Performance of Mask R-CNN Using TensorRT
- Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow
- Notes: From Faster R-CNN to Mask R-CNN
Keras Mask R-CNNAnd, second, how to train a model from scratch and use it to build a smart color splash filter. Including the dataset I built and the trained model. Follow along! Instance segmentation is the task of identifying object outlines at the pixel level. Consider the following asks:. Mask R-CNN regional convolutional neural network is a two stage framework: the first stage scans the image and generates proposals areas likely to contain an object. And the second stage classifies the proposals and generates bounding boxes and masks. This is a standard convolutional neural network typically, ResNet50 or ResNet that serves as a feature extractor. The early layers detect low level features edges and cornersand later layers successively detect higher level features car, person, sky. Passing through the backbone network, the image is converted from xpx x 3 RGB to a feature map of shape 32x32x This feature map becomes the input for the following stages. The code supports ResNet50 and ResNet While the backbone described above works great, it can be improved upon. FPN improves the standard feature extraction pyramid by adding a second pyramid that takes the high level features from the first pyramid and passes them down to lower layers. By doing so, it allows features at every level to have access to both, lower and higher level features. The section after building the ResNet. RPN introduces additional complexity: rather than a single backbone feature map in the standard backbone i. We pick which to use dynamically depending on the size of the object. The RPN is a lightweight neural network that scans the image in a sliding-window fashion and finds areas that contain objects. The regions that the RPN scans over are called anchors. Which are boxes distributed over the image area, as show on the left. This is a simplified view, though. In practice, there are about K anchors of different sizes and aspect ratios, and they overlap to cover as much of the image as possible. How fast can the RPN scan that many anchors? Pretty fast, actually. The sliding window is handled by the convolutional nature of the RPN, which allows it to scan all regions in parallel on a GPU. Instead, the RPN scans over the backbone feature map. This allows the RPN to reuse the extracted features efficiently and avoid duplicate calculations. The RPN generates two outputs for each anchor:. Using the RPN predictions, we pick the top anchors that are likely to contain objects and refine their location and size. If several anchors overlap too much, we keep the one with the highest foreground score and discard the rest referred to as Non-max Suppression. After that we have the final proposals regions of interest that we pass to the next stage. There is a bit of a problem to solve before we continue. They typically require a fixed input size. ROI pooling refers to cropping a part of a feature map and resizing it to a fixed size. If you stop at the end of the last section then you have a Faster R-CNN framework for object detection. The mask branch is a convolutional network that takes the positive regions selected by the ROI classifier and generates masks for them. The generated masks are low resolution: 28x28 pixels. But they are soft masks, represented by float numbers, so they hold more details than binary masks.
A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
Mask RCNN is a deep neural network designed to address object detection and image segmentation, one of the more difficult computer vision challenges. The Mask RCNN model generates bounding boxes and segmentation masks for each instance of an object in the image. This tutorial uses tf. TPUEstimator to train the model. Use the pricing calculator to generate a cost estimate based on your projected usage. New Google Cloud users might be eligible for a free trial. Before you begin Before starting this tutorial, check that your Google Cloud project is correctly set up. If you don't already have one, sign up for a new account. Go to the project selector page. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project. This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges. Open Cloud Shell. Configure gcloud command-line tool to use the project where you want to create the Cloud TPU. This Cloud Storage bucket stores the data you use to train your model and the training results. The ctpu up tool used in this tutorial sets up default permissions for the Cloud TPU service account. If you want finer-grain permissions, review the access level permissions. VMs and TPU nodes are located in specific zoneswhich are subdivisions within a region. The configuration you specified appears. Enter y to approve or n to cancel. When the ctpu up command has finished executing, verify that your shell prompt has changed from username projectname to username vm-name. This change shows that you are now logged into your Compute Engine VM. This tutorial requires a long-lived connection to the Compute Engine instance. To ensure you aren't disconnected from the instance, run the following command:. Add an environment variable for your storage bucket. Replace bucket-name with your bucket name. This installs the required libraries and then runs the preprocessing script. After you convert the data into TFRecords, copy them from local storage to your Cloud Storage bucket using the gsutil command.
Improving the Performance of Mask R-CNN Using TensorRT
Our developers have a keen interest in using image recognition technologies for various purposes. Convolutional neural networks CNNs and machine learning solutions like ImageNet, Facebook facial recognition, and image captioning have already achieved a lot of progress. The main goal of these technologies is to imitate human brain activity to recognize objects in images. For instance, during work on one of our projects concerning practical implementations of convolutional neural networks, we encountered a challenge with increasing Mask R-CNN performance. Over the past few years, deep learning has continued to expand and convolutional neural networks have been released, creating a revolution in image recognition. The CNN is a class of artificial neural network that can be a powerful tool for solving various real-life tasks low traffic detection, human detection, or stationary object detection. In addition to image recognition, CNNs are constantly used for video recognition, recommendation systems, natural language processing, and other applications that involve data with a spatial structure. A CNN is an artificial neural network with a special architecture that uses relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were engineered by hand. A CNN is unidirectional and fundamentally multi-layered. It provides partial resistance to scale changes, offsets, turns, angle changes, and other distortions. Separation from human effort and independence in prior knowledge in future design are the central advantages of this type of network. Region-based convolutional neural networks R-CNNs and fully convolutional networks FCNs are the most recent types of convolutional neural networks. Both have been influential in semantic segmentation and object detectionhelping to solve image processing problems related to detecting sports fieldsdetecting buildingsand generating vector masks from raster data. Based on the previous version, it employs several innovations to improve training and testing speed while also increasing detection accuracy and efficiently classify object proposals using deep convolutional neural networks. Faster R-CNN uses two networks: a region proposal network for generating region proposals and a network for detecting objects. The time cost of generating region proposals is much smaller in a region proposal network than with selective search, when the region proposal network shares the most computation with the object detection network. In short, a region proposal network ranks region boxes called anchors and proposes the ones most likely to contain objects. The first stage is applied to each region of interest in order to get a binary object mask this is a segmentation process. At the first stage, a Mask R-CNN scans the image and generates proposals areas that are likely to contain objects. The second stage operates in parallel with the rest of the neural network responsible for the classification and generation of bounding boxes and masks. A binary mask is calculated for each class, and the final selection is based on the results of the classification. This type of network has shown good results in detection and segmentation as well as in detecting the posture of people. The main benefit of Mask R-CNN is that it provides the best performance among similar solutions in multiple benchmarks and can easily be adjusted for more complex tasks such as processing satellite imagery. This performance is still suitable enough for real-time tasks detecting low traffic, humans, stationary objects, etc. However, its performance may not be enough for certain cases of real-time processing or heavy image processing tasks like those related to satellite imagery. Satellite imagery is high-resolution and requires fast data collection. Moreover, WorldView-3 is able to collect data on up tosquare kilometers per day. The satellite sends a blistering 1. In this case, high-performance solutions are critical. Additionally, this five-frame-per-second performance is true only for low-resolution cameras that gather only light in the visible RGB spectrum which represent only a small part of satellite imagery. However, satellite images are made by high-resolution devices. Because of these two challenges, processing a single satellite image that comes from a modern satellite may take minutes or even hours. By using modern software-as-a-service and distributed computing frameworks, we developed an approach that allows us to boost the performance of state-of-the-art object detection solutions. However, this increase in performance is still not significant for modern quantities of data and speed of data collection. In order to further improve neural network performance, many software solutions have been developed that optimize GPU utilization. These solutions implement software capabilities to use GPU hardware and provide algorithms for distributed computing. With TensorRT, you can optimize neural network models, calibrate for lower precision with high accuracy, and deploy models to hyperscale data centers or embedded or automotive product platforms.
Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow
Author Yusu Pan. LastMod Yuthon's Blog. Independent Masks Multi-task Cascade vs. Localization - Where are they? If it do so, people are likely to mix up them, too. As long as the classifier is precise enough, and we are able to traverse millions of patches in an image, we can always get a satisfactory result. But the amount of calculations is too large. Low amount of calculations. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. Pyramids of images and feature maps are built, and the classifier is run at all scales. Faster R-CNN use pyramids of reference boxes in the regression functions, which avoids enumerating images or filters of multiple scales or aspect ratios. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. Instance Segmentation Use Mask Regression to predict instance segmentation based on object bounding box. Today - details about Mask-RCNN and comparisons RoI Align RoI pooling contains two step of coordinates quantization: from original image into feature map divide by stride and from feature map into roi feature use grid. Those quantizations cause a huge loss of location precision. RoI Align remove those two quantizations, and manipulate coordinates on continuous domainwhich increase the location accuracy greatly. RoI Align really improves the result. Moreover, note that with RoIAlign, using stride C5 features RoIAlign largely resolves the long-standing challenge of using large-stride features for detection and segmentation. Thus many precious work try to find methods to get better results in smaller stride. Now with RoIAlign, we can consider whether to use those tricks. Multinomial vs. Independent Masks Replace softmax with sigmoid. Mask R-CNN decouples mask and class prediction: as the existing box branch predicts the class label, we generate a mask for each class without competition among classes by a per-pixel sigmoid and a binary loss. In Table 2b, we compare this to using a per-pixel softmax and a multinomial loss as com- monly used in FCN. This alternative couples the tasks of mask and class prediction, and results in a severe loss in mask AP 5. The result suggests that once the instance has been classified as a whole by the box branchit is sufficient to predict a binary mask without concern for the categorieswhich makes the model easier to train. Multi-task Cascade vs. Joint Learning Cascading and paralleling are adopted alternately. But on testing time, we do classification and bbox regression first, and then use those results to get masks. BBox regression may change the location of bbox, so we should wait it to be done. After bbox regression, we may adopt NMS or other methods to reduce the number of boxes.