Need a model that can distinguish between large number of people or objects by using only a small number of sample images? Clarifai_ResNet_AngularMargin is here to help.
Clarifai_ResNet_AngularMargin consistently outperforms the state-of-the-art and has been implemented with efficient computational overhead so that predictions are made quickly.
Originally conceived asa a state-of-the-art face recognition method for obtaining highly discriminative features for face recognition, Clarifai_ResNet_AngularMargin has become a popular solution for many "visual search" use cases where accurate search results can be obtained by exaggerating the fine differences between objects. Build your firstClarifai_ResNet_AngularMargin model today.
Model efficiency has become increasingly important in computer vision. Clarifai_EfficientDet is built on a new family of object detectors that have been designed for scalable and efficient object detection.
The Clarifai_EfficientDet visual detector enables you to build deep-trained models that are lightweight and fast. Clarifai_EfficientDet makes a good choice for an algorithm to run on edge devices, trains quickly and keeps inference response time kept at a minimum.
Clarifai_EfficientDet uses a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Deep train with Clarifai_EfficientDet.
Our new and improved polygon labeling tools make it easy to label multi-node polygon images in your labeling projects. You can now insert and edit polygon labels in Scribe more efficiently than ever, so that you can label the pixels of an image that are specific to the object that you would like to annotate. Learn more about polygon labeling.
