export const DEMO_TITLES = { mobilenet : 'MobileNet, trained on ImageNet' , squeezenet : 'SqueezeNet, trained on ImageNet' , emotion_ferplus : 'FER+ Emotion, real-time emotion detection' , yolo : 'Yolo, real-time object detection' , mnist : 'MNIST, handwritten digit prediction' } ; export const DEMO_DESCRIPTIONS = { mobilenet : 'MobileNet models perform image classification - they are also very efficient in terms of speed and size and hence are ideal for embedded and mobile applications.' , squeezenet : 'SqueezeNet is a light-weight convolutional networks for image classification.' , emotion_ferplus : 'FER+ Emotion, a deep convolutional neural network for emotion recognition in faces.' , yolo : 'YOLO can detect multiple objects in an image and puts bounding boxes around these objects.' , mnist : 'MNIST, handwritten digit prediction.' } ; export const DEMO_MODEL_LINKS = { mobilenet : 'https://github.com/onnx/models/tree/master/vision/classification/mobilenet' , squeezenet : 'https://github.com/onnx/models/tree/master/vision/classification/squeezenet' , emotion_ferplus : 'https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus' , yolo : 'https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/tiny-yolov2' , mnist : 'https://github.com/onnx/models/tree/master/vision/classification/mnist' } ;