Making predictions from your models¶

inference infer command offers an easy way to make predictions from your model based on your input images or video files sending requests to inference server, depending on command configuration.

Discovering command capabilities

To check detail of the command, run:

inference infer --help

Command details¶

inference infer takes input path / url and model version to produce predictions (and optionally make visualisation using supervision). You can also specify a host to run inference on our hosted inference server.

Note

If you decided to use hosted inference server - make sure command inference server start was used first

Tip

Roboflow API key can be provided via ROBOFLOW_API_KEY environment variable

Examples¶

Below, you have usage examples illustrated.

Predict On Local Image¶

This command is going to make a prediction from local image using selected model and print the prediction on the console.

inference infer -i ./image.jpg -m {your_project}/{version} --api-key {YOUR_API_KEY}

To display visualised prediction use -D option. To save prediction and visualisation in a local directory, use -o {path_to_your_directory} option. Those options work also in other modes.

inference infer -i ./image.jpg -m {your_project}/{version} --api-key {YOUR_API_KEY} -D -o {path_to_your_output_directory}

Predict On Image URL¶

inference infer -i https://[YOUR_HOSTED_IMAGE_URL] -m {your_project}/{version} --api-key {YOUR_API_KEY}

Using Hosted API¶

inference infer -i ./image.jpg -m {your_project}/{version} --api-key {YOUR_API_KEY} -h https://detect.roboflow.com

Predict From Local Directory¶

inference infer -i {your_directory_with_images} -m {your_project}/{version} -o {path_to_your_output_directory} --api-key {YOUR_API_KEY}

Predict On Video File¶

inference infer -i {path_to_your_video_file} -m {your_project}/{version} -o {path_to_your_output_directory} --api-key {YOUR_API_KEY}

Configure The Visualization¶

Option -c can be provided with a path to *.yml file configuring supervision visualisation. There are few pre-defined configs: - bounding_boxes - with BoxAnnotator and LabelAnnotator annotators - bounding_boxes_tracing - with ByteTracker and annotators (BoxAnnotator, LabelAnnotator) - masks - with MaskAnnotator and LabelAnnotator annotators - polygons - with PolygonAnnotator and LabelAnnotator annotators

Custom configuration can be created following the schema:

annotators:
  - type: "bounding_box"
    params:
      thickness: 2
  - type: "label"
    params:
      text_scale: 0.5
      text_thickness: 2
      text_padding: 5
  - type: "trace"
    params:
      trace_length: 60
      thickness: 2
tracking:
  track_activation_threshold: 0.25
  lost_track_buffer: 30
  minimum_matching_threshold: 0.8
  frame_rate: 30

annotators field is a list of dictionaries with two keys: type and param. type points to name of annotator class:

from supervision import *
ANNOTATOR_TYPE2CLASS = {
    "bounding_box": BoxAnnotator,
    "box": BoxAnnotator,
    "mask": MaskAnnotator,
    "polygon": PolygonAnnotator,
    "color": ColorAnnotator,
    "halo": HaloAnnotator,
    "ellipse": EllipseAnnotator,
    "box_corner": BoxCornerAnnotator,
    "circle": CircleAnnotator,
    "dot": DotAnnotator,
    "label": LabelAnnotator,
    "blur": BlurAnnotator,
    "trace": TraceAnnotator,
    "heat_map": HeatMapAnnotator,
    "pixelate": PixelateAnnotator,
    "triangle": TriangleAnnotator,
}

param is a dictionary of annotator constructor parameters (check them in supervision docs - you would only be able to use primitive values, classes and enums that are defined in constructors may not be possible to resolve from yaml config).

tracking is an optional key that holds a dictionary with constructor parameters for ByteTrack.

Provide Inference Hyperparameters¶

-mc parameter can be provided with path to *.yml file that specifies model configuration (like confidence threshold or IoU threshold). If given, configuration will be used to initialise InferenceConfiguration object from inference_sdk library. See sdk docs to discover which options can be configured via *.yml file - configuration keys must match with names of fields in InferenceConfiguration object.