📄️ Basic Classification
This example loads a complete classification pipeline file (.denkflow) exported from the Hub.
📄️ Basic Object Detection
This example loads a complete object detection pipeline file (*.denkflow) exported from the Hub.
📄️ Basic Segmentation
This example loads a complete segmentation pipeline file (.denkflow) exported from the Hub.
📄️ Basic Instance Segmentation
This example loads a complete instance segmentation pipeline file (.denkflow) exported from the Hub. An instance segmentation network returns bounding box tensors and segmentation tensors, which need to be combined in the to_objects-function.
📄️ Optical Character Recognition (OCR)
This example shows how to load a pre-built OCR pipeline from a .denkflow file.
📄️ Ready-To-Use OCR Package
This is a ready-to-use example. All authentication, models, images and example Dockerfile are included. Download it here:
📄️ Configuration Options
The Pipeline.fromdenkflow(filename, kwargs) method is the primary way to load pre-built .denkflow pipelines. Here are several configuration options for different scenarios:
📄️ Custom Pipeline Construction
This example shows how to build a pipeline manually, adding an object detection node that uses an .denkmodel model file.
📄️ Docker Deployment
Running the DENKflow API in a Docker container requires careful handling of the data directory for persistence, especially with OneTimeLicenseSource or TensorRT caching. The volumes that need to be mounted are listed in the Configuration Section.
📄️ Using NumPy Arrays
This example loads a complete classification pipeline file (.denkflow) exported from the Hub. Instead of reading the image data from a file, it is instead read via OpenCV, which produces a NumPy array. This array is then converted to an ImageTensor using the fromnumpyopencv method. NumPy arrays produced by OpenCV have the shape [Height, Width, Channels], which is what the function expects. You can also use the from_numpy method, which expects a NumPy array of dtype float32 with the values normalized to the range [0, 1] and the shape [Batch Size, Channels, Height, Width].