Noble Shining Cardinal

from pictograph import Client, get_model, InstanceSegmentationModel, InstanceSegmentationResult

client = Client(api_key="pk_live_...")   # Settings -> API Keys

# Downloads + caches the weights on first use
model: InstanceSegmentationModel = get_model(
    name="Noble Shining Cardinal",
    task="instance_segmentation",
    format="onnx",
    client=client,
)
result: InstanceSegmentationResult = model.predict(image="photo.jpg")

for p in result.predictions:
    print(p.name, round(p.confidence, 2), p.bounding_box)

from pictograph import load_model, InstanceSegmentationModel, InstanceSegmentationResult

# Download the weights + config with the buttons above
model: InstanceSegmentationModel = load_model(
    weights="/path/to/weights.onnx",
    config="/path/to/config.json",
    task="instance_segmentation",
    format="onnx",
    device="auto",
)
result: InstanceSegmentationResult = model.predict(image="photo.jpg")

for p in result.predictions:
    print(p.name, round(p.confidence, 2), p.bounding_box)

curl -X POST "https://workflows.pictograph.io/your-endpoint/predict" \
  -H "Authorization: Bearer pk_deploy_..." \
  -F "file=@frame.jpg"

from pictograph import DeploymentClient, InstanceSegmentationResult

client = DeploymentClient(
    endpoint="https://workflows.pictograph.io/your-endpoint/predict",
    api_key="pk_deploy_...",  # from your deployment's settings
    task="instance_segmentation",
)
result: InstanceSegmentationResult = client.infer(image="frame.jpg")

for p in result.predictions:
    print(p.name, round(p.confidence, 2), p.bounding_box)

pictograph deployments predict \
  <deployment-id> \
  frame.jpg \
  --token pk_deploy_... \
  --endpoint "https://workflows.pictograph.io/your-endpoint/predict"