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Polygon and nothing else. They support some hotkey shortcuts and the application is very lightweight in general.3. Supervise.lySupervisely is an awesome web-based platform that offers an advanced annotation interface but also covers the entire process of computer vision training, including a deep learning models library that can be directly trained, tested, and improved within the platform.Price: Free community edition and enterprise pricing for the self-hosted versionFunctionalities: A great array of tools, including dots, lines, boxes, polygons, and a bitmap brush for semantic segmentation (we haven’t found their smart tool too useful though). Also includes the possibility to draw holes in polygons, which has been incredibly valuable. Another very useful feature is the option to add image and object tags and to order figures in layers. Output is in JSON files for each image or PNG masks and the platform also allows you to upload formats such as Cityscapes and COCO. In addition, there is an option to do data transformation directly on the platform.Project management: The platform offers tons of options for project management on various levels (teams, workspaces, datasets) and annotator management (labeling jobs, permissions, statistics). They also have a Data Transformation Language and a Python Notebooks option for managing the data which come in handy a lot. A couple of things missing are time statistics, as well as quality control mechanisms. Their tech support team is always available in the case of problems. The interface allows for very precise work and supports customizable hotkey shortcuts but performance has recently been slow at times, which can be pretty frustrating if the platform takes a lot of time to switch between images and record annotations.4. LabelboxLabelbox is another great web-based platform that launched in early 2018 and ever since then has been constantly updating and improving its functionalities. It also offers the possibility to integrate a human-in-the-loop by importing model predictions and seeing the consensus between the labelers and the model.Pricing: Free community edition limited to 5000 images and an enterprise versionFunctionalities: Offers a complete array of tools for annotation, such as points, lines, boxes and polygons, and has recently

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Added an awesome new feature for their semantic segmentation brush — a superpixel coloring option that makes life so much easier when boundaries are clear (much like this and this open source tools). Output is as one JSON or CSV file containing all annotations or as PNG masks (however, there is one mask for every class and the user needs to figure out what to do with overlapping regions afterwards)Project management: Setting up a project is extremely easy, and there are many options for monitoring performance, including statistics on seconds needed to label an image. You can implement several quality control mechanisms, including activating automatic consensus between different labelers or setting gold standard benchmarks. You have the option to invite users (though permissions are not as granular) and to review the work of each one. The labeling interface is super user-friendly and supports hotkey shortcuts (although not customizable). One thing missing in the free version is the option to upload annotations so as to visualize or edit them.Need something else? Here are some other platforms that you can consider:Diffgram — a really promising platform still in beta that optimizes image annotation by training RCNN, will be featured in the second article in this series!RectLabel — an awesome tool for bounding boxes and polygons for MacOSProdigy — they offer a self-hosted back-end with different annotation interfaces, including image annotation with bounding boxes; pricing for their product starts at $390 for personal use (lifetime per user)DataTurks — a platform that offers many annotation capabilities; data annotated in the free version is publicly available, and enterprise pricing starts at $300 per month for small teamsImageTagger — an open source platform for collaborative image labelingFast Annotation Tool — another open source tool, using OpenCV for bounding boxes in the RotatedRect formatLabelMe — an industry classic, open source tool by MIT for polygonal annotation; precision is extremely low thoughPolygonRNN+ — available only as a demo, but still very promising; a tool that is trained on the Cityscapes dataset do generate automatic labels for self-driving cars with reinforcement learning—Dealing with large datasets and need to scale

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GazeNet Model CardModel Overview The model described in this card detects a person's eye gaze point of regard (X, Y, Z) and gaze vector (theta and phi). The eye gaze vector can also be derived from eye position and eye gaze points of regard.Model Architecture GazeNet is a multi-input and multi-branch network. The four input for GazeNet consists: Face crop, left eye crop, right eye crop, and facegrid. Face, left eye, and right eye branch are based on AlexNet as feature extractors. The facegrid branch is based on fully connected layers. Please see the paper in the citations for an example of the model architecture.Training Algorithm This model was trained using the GazeNet entrypoint in TAO. The training algorithm optimizes the network to minimize the root mean square error between predicted and ground truth point of regards.Training Data GazeNet trainable model was trained on a proprietary dataset with more than 220K Images. The training dataset consists of images taken from cameras mounted at varied heights and angles and with various illumination conditions.Training Data Ground-truth Labeling Guidelines The training dataset is created by labeling ground-truth bounding-boxes and landmarks by human labelers. he face bounding box and fiducial landmarks are used to prepare inputs (face crop image, left eye crop image, right eye crop image, and facegrid) to the gaze model. For Face bounding boxes labeling, please refer to the FaceNet model card. For Facial landmarks labeling, please refer to the FPENet model card.During GazeNet data collection, we asked subjects to look at a dot on the screen, while collecting image data. The X, Y, Z position (point of regard) of the dot in the camera coordinate system are obtained through calibration between camera and screen.PerformanceEvaluation Data Dataset The inference performance of GazeNet v1.0 model was measured against 110K proprietary images across a variety of subjects, illuminitation conditions, camera heights and camera angles.Methodology and KPI The key performance indicators (KPI) is the error of point of regard. Point of regard is the point at which the eye is looking.GazeNetKPIContentPosition X ErrorPosition Y ErrorPosition Z ErrorPosition XYZ ErrorPosition XY ErrorThetaPhiTheta-Phi(in cm)(in cm)(in cm)(in cm)(in cm)(in degree)(in degree)(in degree)Evaluation set3.553.931.666.295.922.482.453.9Real-time Inference Performance The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.DevicePrecisionBatch_sizeFPSNanoFP16187NXFP161510XavierFP161704T4FP1611698How to use this model This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream 6.0 or TensorRT.Primary use case for this model is to detect eye point of regard and gaze vector. The model can be used to detect eye gaze point of regard by using appropriate video or image decoding and pre-processing.See the following image for an illustration of eye gaze estimation usage.There are

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