DeepDetect Server Documentation
The document covers the topics below:
Setup an image classifier: tag any image from the Web with state of the art Deep Learning
Setup an object detector: predict a bounding box around objects of interest and tag each of them
Running on Raspberry Pi: run deep nets on Raspberry Pi (and other similar devices) with up to 3 FPS for object detection, and support for image classification and OCR as well
Train a service from a CSV dataset: very common generic use case, use a multilayer neural network, here to predict trees’ cover type
Train your own image classifier: use state of the art Deep Learning to build a service that recognizes images
Train from text: use a multilayer neural network to classify text files into several categories
Multi-GPU training: use multiple GPUs to scale up the training or large deep neural networks
Robust image model training with Data Augmentation: use random noise and distortions of images to make image classification and object detection models more robust, e.g. to user generated content