SentiSight.ai is a web-based platform that can be used for image labeling and for developing AI-based image recognition applications. It has two major goals: the first is to make the image annotation task as convenient and efficient as possible, even for large projects with many people working on image labeling, and the second is to provide a smooth and user-friendly interface for training and deploying deep neural network models. The ability to perform both of these tasks on the same platform provides the advantage of being able to label images and then train and improve models in an iterative way.
SentiSight.ai offers powerful features, such as:
Image labeling. Our labeling tool allows adding classification labels, bounding boxes, polygons, points, polylines, and bitmaps. Bitmaps can be easily converted to polygons and vice versa. Moreover, each labeled object can have several child objects, such as key-points or attributes. The labeled images can be directly used for model training on the SentiSight.ai platform, or they can be downloaded and used for in-house model training.
Smart labeling tool. This tool can be used to significantly increase the speed of bitmap labeling. The smart labeling tool allows users to select a few points in the foreground and the background and let the AI extract the labeled object.
Shared labeling projects and time tracking. To make large annotation project handling easier, SentiSight.ai allows a project to be shared among multiple users so that multiple people can label images in the same project. The project manager can quickly filter and review the images labeled by a particular project member, track each person’s progress and time spent on labeling, as well as manage user roles and permissions.
Classification model training. This type of model can be used to identify certain objects in an image, such as a cat or a dog, but without specifying their location. They can also be trained to identify more abstract concepts, such as “summer” or “winter”.
Object detection model training. This type of model can be used not to only identify a certain object, but also to predict its exact location in an image. For each object predicted to be inside the image, the model also predicts a rectangular bounding box that denotes the object’s location. This is very useful when you need to know not only what is inside the image, but also the relative location and number of objects.
Online and offline models (free 30-day trial available). SentiSight.ai offers a possibility to use your deep learning models both online and offline. Online models can be used via REST API or web interface. Both of these options require internet connection. Another option is to download and use the image recognition model offline. An offline model can be downloaded as a free 30-day trial after which the user has an option to buy a license. The price of the license depends on the speed of the model, and it is a single time payment.
Pre-trained models. In addition to the possibility of training image recognition models yourself, SentiSight.ai also provides several pre-trained models that can be used out-of-the-box without any additional training. These pre-trained models can be used for several tasks, such as content moderation, goods classification, automatic hashtags, people counting and more.
Image Similarity search. This is another ready-to-use feature that allows users to upload an image and find all similar images to this query in their data set. It also allows users to perform NvN similarity searches in their data set where all similar image pairs are retrieved.
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Overview by
Martynas Kudarauskas
