Currently Neural Networks are the methods of choice to deal with most computer vision tasks. With Supervisely, you can train neural networks on your images, control and visualize training process. After the model is ready, you can apply it to recognize your images or download the model and use it in your products.

Neural networks are at the heart of Supervisely and extend Supervisely functionality in a following ways:

  • Access to State-Of-The-Art models. Access to State-Of-The-Art models. You will find a large collection of State-Of-The-Art models in Supervisely model Zoo that you can train and use in your products. Some of the available models are listed below:

    • UNet
    • Mask R-CNN
    • YOLO v3
    • DeepLab v3
    • MobileNet SSD
    • PSPNet
    • ICNet
    • Faster-RCNN
    • and others
  • Research automatization. Building a neural network that provides desired levels of accuracy might require a lot of “training procedures”. It is very important to treat the process systematically - to be able to save, review and reproduce the results of experiments, share results with other team members. The Supervisely was designed to automate research processes

  • Human-in-the-loop. To perform image annotation faster, human-in-the-loop approach is known to be effective.

  • Usage of pre-trained models. Often, a computer vision system is built to handle "well-known" objects, such as people or cars. For such cases, there are number of pre-modeled models in the model zoo. These models can be used as a ready-to-use-component through API, or for speeding up the annotation process.

  • Integration of custom models. Docker technology make it easy to integrate your custom neural networks into Supervisely. Just implement simple API and put your code into docker image.

  • Optimize Smart tool to segment specific objects on images. Smart Tool allows to do pixelwise annotation with minimum number of clicks. To make Smart Tool adaptation for your specific object you can train it within Supervisely.

Please use left menu of documentation to read more about neural networks section.