Import Predictions

Overview

To upload predictions in Encord Active, you need to create a prediction branch. This guide explains everything you need to know for importing predictions.

Predictions Workflow

1. Import Predictions to Annotate Project: Everything starts in Annotate. Your labels and predictions must exist in your Annotate Project for the labels and predictions to appear in Active.
2. Import/Sync Project in Active: After importing your predictions, you can then import the Project into Active. Or you can sync an existing Active Project after importing your predictions.
3. Analyze the Predictions in Active: Once the Project import/sync completes, specify the prediction set for Active to analyze.
4. Select the Predictions in Active: Once analysis completes, select the prediction set you want to view in Active.

Supported Prediction Formats

Encord Format (Recommended)

• Supports multi-level nested classifications (radio, checklist, or free-form text) under objects or classifications.
• Handles all object types and classification.

• Only top-level objects and classifications are considered when calculating in model metrics.

• Metrics are not yet available for keypoints and polylines. If you are interested in these, please contact the Encord team.

COCO Format

Does not supports multiple levels of nested classifications (radio, checklist, or free-form text) under tools or classifications.

Confidence Score

You can include confidence scores when uploading predictions. Encord automatically calculates model metrics based on your prediction set and assigned confidence scores.

Prediction Branches

When importing prediction sets into Encord Active, they are added as branches to individual label rows on your data units (images, videos, audio). Each data unit has the following:

• A MAIN branch for ground truth annotations or pre-labels.
• Optional Consensus branches and Prediction branches for different prediction sets.

You can export main branch labels from the UI. Consensus labels and model predictions can only be exported using the SDK.

STEP 1: Import Predictions

Import your predictions to a Project in Annotate. Encord currently supports importing predictions from the Encord format and from COCO.

TLDR;

Do you already know what you are doing and only want to look over a Jupyter Notebook example to import your predictions? We provide one here.

Import Encord-Format Predictions

Use branch_name to create a prediction branch in label_rows_v2 for a data unit.

• branch_name supports alphanumeric characters (a-z, A-Z, 0-9) and is case sensitive
• branch_name supports the following special characters: hyphens (-), underscores (_), and periods (.)

Bounding Box

Example 1

Imports a single bounding box (Cherry) to a single image (cherry_001.png).

Example 2:

Imports three instances (tracking an object across three sequential frames: 103, 104, and 105) of a bounding box (Cherry) to a video (Cherries_video.mp4).

Example 3

Imports three bounding boxes (Cherry) to a single image (cherry_001.png).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 103, 104, 105, object 2 - frames 206, 207, 208, and object 3 - frames 313, 315, 317) of three bounding boxes (Cherry) to a video (Cherries_video.mp4).

Bounding Box Template

# Import dependencies
import os
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import LabelRowV2, Object, OntologyStructure, ObjectInstance
from encord.objects.coordinates import BoundingBoxCoordinates, RotatableBoundingBoxCoordinates, PolygonCoordinates, PolylineCoordinates, PointCoordinate, BitmaskCoordinates

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"
PROJECT_HASH = "<unique-id-of-your-project>"
PREDICTION_BRANCH_NAME = "<name-of-your-prediction-branch>"
ONTOLOGY_OBJECT_TITLE = "<name-of-ontology-object>" # Name of object label in your Ontology
DATA_UNIT_TITLES = ["<data-unit-title-1>", "<data-unit-title-2>"]  # List of specific data units

# Ensure SSH path and project hash are set
assert SSH_PATH, "SSH path cannot be None"
assert PROJECT_HASH, "Project hash cannot be None"

# Authenticate with Encord using access key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=Path(SSH_PATH).read_text()
)

# Get the project and list label rows for the prediction branch
project = user_client.get_project(PROJECT_HASH)
all_prediction_branch_rows = project.list_label_rows_v2(branch_name=PREDICTION_BRANCH_NAME)

# Filter label rows based on the specified data unit titles
prediction_branch_rows = [
    row for row in all_prediction_branch_rows 
    if row.data_title in DATA_UNIT_TITLES
]

if not prediction_branch_rows:
    print("No matching data units found in the specified branch.")
else:
    print("Data units found:", [row.data_title for row in prediction_branch_rows])

# Retrieve the specified ontology object by title
ontology_object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE,
    type_=Object
)

# Initialize labels for each selected label row in the prediction branch
with project.create_bundle() as bundle:
    for row in prediction_branch_rows:
        row.initialise_labels(bundle=bundle)

# Add bounding box predictions to each filtered label row
for row in prediction_branch_rows:
    # Instantiate an object instance for bounding box predictions
    inst = ontology_object.create_instance()
    inst.set_for_frames(
        coordinates=BoundingBoxCoordinates(
            height=0.1,
            width=0.1,
            top_left_x=0.5,
            top_left_y=0.5,
        ),
        frames=0,  # Apply to the specified frame
        manual_annotation=False,  # Set to False as this is a prediction
        confidence=1, # Confidence of your prediction
    )

    # Add the prediction instance to the label row
    row.add_object_instance(inst)

with project.create_bundle() as bundle:
    # Save the row with predictions within the bundle
    for row in prediction_branch_rows:
        row.save(bundle=bundle)

Rotatable Bounding Box

Example 1

Imports a single rotatable bounding box (Other type of fruit) to a single image (apple_001.png).

Example 2

Imports three instances (tracking an object across three sequential frames: 120, 121, and 122) of a bounding box (Other type of fruit) to a video (Cherries_video.mp4).

Example 3

Imports three rotatable bounding boxes (Other type of fruit) to a single image (apple_001.png).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 120, 121, 122, object 2 - frames 222, 224, 226, and object 3 - frames 321, 323, 325) of three rotatable bounding boxes (Other type of fruit) to a video (Cherries_video.mp4).

Import Rotatable Bounding Box Template

# Import dependencies
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import RotatableBoundingBoxCoordinates

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"
PROJECT_HASH = "<unique-hash-for-project>"
PREDICTION_BRANCH_NAME = "<name-of-your-prediction-branch>"
ONTOLOGY_OBJECT_TITLE = "<rotatable-bounding-box-class-title>"
DATA_UNIT_TITLES = ["<data-unit-title-1>", "<data-unit-title-2>"]  # Specify the data unit titles here

# Authenticate with Encord using access key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=Path(SSH_PATH).read_text()
)

# Get the project and list label rows for the prediction branch
project = user_client.get_project(PROJECT_HASH)
all_prediction_branch_rows = project.list_label_rows_v2(branch_name=PREDICTION_BRANCH_NAME)

# Filter label rows based on the specified data unit titles
prediction_branch_rows = [
    row for row in all_prediction_branch_rows 
    if row.data_title in DATA_UNIT_TITLES
]

if not prediction_branch_rows:
    print("No matching data units found in the specified branch.")
else:
    print("Data units found:", [row.data_title for row in prediction_branch_rows])

# Retrieve the specified ontology object by title
rbb_ontology_object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE,
    type_=Object
)

with project.create_bundle() as bundle:
    # Initialize labels for each selected label row in the prediction branch
    for label_row in prediction_branch_rows:
        label_row.initialise_labels(bundle=bundle)

# Define rotatable bounding box coordinates and frame for each object
rotatable_bounding_box_predictions = [
    {
        "frame_number": 0,
        "coordinates": RotatableBoundingBoxCoordinates(
            height=0.3,
            width=0.2,
            top_left_x=0.1,
            top_left_y=0.1,
            theta=15  # Angle of rotation in degrees
        ),
    },
    {
        "frame_number": 5,
        "coordinates": RotatableBoundingBoxCoordinates(
            height=0.25,
            width=0.25,
            top_left_x=0.15,
            top_left_y=0.15,
            theta=30
        ),
    },
    {
        "frame_number": 10,
        "coordinates": RotatableBoundingBoxCoordinates(
            height=0.2,
            width=0.3,
            top_left_x=0.2,
            top_left_y=0.2,
            theta=45
        ),
    },
]

# Add rotatable bounding box predictions to each filtered label row
for label_row in prediction_branch_rows:
    for prediction in rotatable_bounding_box_predictions:
        rbb_object_instance = rbb_ontology_object.create_instance()
        
        rbb_object_instance.set_for_frames(
            coordinates=prediction["coordinates"],
            frames=prediction["frame_number"],
            manual_annotation=False,  # Mark as a prediction
            confidence=1.0
        )
        
        # Link the object instance to the label row
        label_row.add_object_instance(rbb_object_instance)
    
with project.create_bundle() as bundle:
    for label_row in prediction_branch_rows:
        # Save the label row with the updated predictions within the bundle
        label_row.save(bundle=bundle)

Polygons

Example 1

Imports a single polygon (Persimmon) to a single image (persimmon_001.jpg).

Example 2

Imports three instances (tracking an object across three sequential frames: 143, 144, and 145) of a polygon (Persimmon) to a video (Cherries_video.mp4).

Example 3

Imports three polygons (Persimmon) to a single image (persimmon_001.jpg).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 153, 154, 155, object 2 - frames 242, 244, 246, and object 3 - frames 343, 345, 347) of three polygons (Persimmon) to a video (Cherries_video.mp4).

Import Polygon Template

# Import dependencies
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import PolygonCoordinates, PointCoordinate

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"
PROJECT_HASH = "<unique-project-hash>"
PREDICTION_BRANCH_NAME = "<name-of-your-prediction-branch>"  # Specify the prediction branch
DATA_UNIT_TITLE = "<name-of-data-unit>"  # The title of the data unit (image or video)
ONTOLOGY_OBJECT_TITLE = "<object-name>"  # The ontology object title for the polygon

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get the project and list label rows for the prediction branch
project: Project = user_client.get_project(PROJECT_HASH)
prediction_branch_rows = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Use the prediction branch
    data_title_eq=DATA_UNIT_TITLE
)

if not prediction_branch_rows:
    print("No matching data units found in the specified branch.")
else:
    print("Data units found:", [row.data_title for row in prediction_branch_rows])

# Initialize labels for each selected label row in the prediction branch
label_row = prediction_branch_rows[0]
label_row.initialise_labels()

# Find a polygon annotation object in the project ontology
polygon_ontology_object: Object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE, type_=Object
)

# Instantiate an object instance from the polygon ontology node
polygon_object_instance: ObjectInstance = polygon_ontology_object.create_instance()

# Define the polygon coordinates
polygon_coordinates = PolygonCoordinates([
    PointCoordinate(0.1, 0.2),
    PointCoordinate(0.3, 0.4),
    PointCoordinate(0.5, 0.6),
    PointCoordinate(0.7, 0.8)
])

# Set the polygon prediction for the specified frame
polygon_object_instance.set_for_frames(
    coordinates=polygon_coordinates,
    frames=0,  # Specify the frame number
    manual_annotation=False,  # Mark as a prediction
    confidence=1.0  # Optional confidence score
)

# Link the object instance to the label row
label_row.add_object_instance(polygon_object_instance)

# Save the label row 
label_row.save()

Polyline

Example 1

Imports a single polyline (Branch) to a single image (persimmon_001.jpg).

Example 2

Imports three instances (tracking an object across three sequential frames: 146, 147, and 148) of a polygon (Branch) to a video (Cherries_video.mp4).

Example 3

Imports three polylines (Branch) to a single image (persimmon_001.jpg).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 246, 247, 248, object 2 - frames 346, 347, 348, and object 3 - frames 446, 447, 448) of three polylines (Branch) to a video (Cherries_video.mp4).

Import Polyline Template

# Import dependencies
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import PolylineCoordinates, PointCoordinate

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"  # Path to your SSH private key
PROJECT_HASH = "<unique-project-hash>"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "<name-of-data-unit>"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "<object-name>"  # Name of the polyline object in your ontology

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to label in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row 
label_row.initialise_labels()

# Find a polyline annotation object in the project ontology
polyline_ontology_object: Object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE, type_=Object
)

# Instantiate an object instance from the polyline ontology node
polyline_object_instance: ObjectInstance = polyline_ontology_object.create_instance()

# The x,y coordinates of each polyline vertex are specified as follows
polyline_object_instance.set_for_frames(
    coordinates=PolylineCoordinates(
        [PointCoordinate(.x1, .y1), PointCoordinate(.x1, .y1), PointCoordinate(.x3, .y3), PointCoordinate(.13, .456)]
    ),
    # Add the polyline to the specified frame number
    frames=<frame-number>,  # Replace with the actual frame number
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the object instance to the label row
label_row.add_object_instance(polyline_object_instance)

# Save the label row with the polyline instance
label_row.save()
print("Label row updated with polyline instance.")

Keypoint

Example 1

Imports a single keypoint (Pedicel) to a single image (blueberry_003.png).

Example 2

Imports three instances (tracking an object across three sequential frames: 143, 144, and 145) of a keypoint (Pedicel) to a video (Blueberries_video.mp4).

Example 3

Imports three keypoints (Pedicel) to a single image (blueberry_003.png).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 143, 144, 145, object 2 - frames 242, 244, 246, and object 3 - frames 343, 345, 347) of three keypoints (Pedicel) to a video (Blueberries_video.mp4).

Import Keypoint Template

# Import dependencies
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import PointCoordinate

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"  # Path to your SSH private key
PROJECT_HASH = "<unique-project-hash>"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "<name-of-data-unit>"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "<object-name>"  # Name of the keypoint object in your ontology

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to label in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row
label_row.initialise_labels()

# Find a keypoint annotation object in the project ontology
keypoint_ontology_object: Object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE, type_=Object
)

# Instantiate an object instance from the keypoint ontology node
keypoint_object_instance: ObjectInstance = keypoint_ontology_object.create_instance()

# The x,y coordinates of the keypoint are specified as follows
keypoint_object_instance.set_for_frames(
    coordinates=PointCoordinate(
        x=<value-for-x-axis>,  # Replace with the actual value for the x-axis
        y=<value-for-y-axis>   # Replace with the actual value for the y-axis
    ),
    # Add the keypoint to the specified frame number
    frames=<frame-number>,  # Replace with the actual frame number
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the object instance to the label row
label_row.add_object_instance(keypoint_object_instance)

# Save the label row with the keypoint instance
label_row.save()
print("Label row updated with keypoint instance.")

Bitmask

Example 1:

Imports a single bitmask (Blueberry) to a single image (blueberry_003.jpg). For simplicity, the bitmask covers the entire image (image dimensions: 1254x836).

Example 2:

Imports three instances (tracking an object across three sequential frames: 156, 157, and 159) of a bitmask (Blueberry) to a video (Blueberries_video.mp4). For simplicity, the bitmask covers the entire frame (video dimensions: 1920x1080).

Example 3:

Imports three bitmasks (Blueberry) to a single image (blueberry_003.jpg). For simplicity, the bitmasks cover the entire image (image dimensions: 1254x836).

Example 4:

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 156, 157, 158, object 2 - frames 256, 258, 259, and object 3 - frames 355, 357, 359) of three bitmasks (Blueberry) to a video (Blueberries_video.mp4). For simplicity, the bitmasks cover the entire frame (video dimensions: 1920x1080).

Template

# Import dependencies
import numpy as np
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import BitmaskCoordinates

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"  # Path to your SSH private key
PROJECT_HASH = "<unique-project-hash>"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "<data-unit-name>"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "<bitmask-object-name>"  # Name of the bitmask object in your ontology

# Prepare the mask itself.
# For simplicity, we can just mask the whole image
# Note: the size of the mask must be identical to the size of the image
numpy_coordinates = np.ones((<y-axis-value>, <x-axis-value>))  # Replace with actual values

# Ensure the image is in boolean format
numpy_coordinates = numpy_coordinates.astype(bool)

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to label in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row 
label_row.initialise_labels()

# Find a bitmask annotation object in the project ontology
bitmask_ontology_object: Object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE, type_=Object
)

# Instantiate an object instance from the bitmask ontology node
bitmask_object_instance: ObjectInstance = bitmask_ontology_object.create_instance()

# The coordinates for the bitmask are specified as follows
bitmask_object_instance.set_for_frames(
    # Create coordinates from provided numpy bitmask
    coordinates=BitmaskCoordinates(numpy_coordinates),
    # Add the bitmask to the first frame
    frames=0,
    # There are multiple additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the object instance to the label row
label_row.add_object_instance(bitmask_object_instance)

# Save the label row with the bitmask instance
label_row.save()
print("Label row updated with bitmask instance.")

Object Primitives

Before you can import Object Primitive labels into Encord, the Object Primitive Template MUST exist in Encord. Use the UI to create the Object Primitive Template so you can visually inspect the Object Primitive.

Import Object Primitive labels

Example 1

Imports a single object primitive (Ontology object = Strawberry Object Primitive name = Triangle) to a single image (strawberries_10.jpg).

Example 2

Imports three instances (tracking an object across three sequential frames: 163, 164, and 165) of a object primitive (Ontology object = Strawberry Object Primitive name = Triangle) to a video (Cherries_video.mp4).

Example 3

Imports three object primitives (Ontology object = Strawberry Object Primitive name = Triangle) to a single image (strawberries_10.jpg).

Example 4

Imports three instances (tracking 3 different objects across three frames: object 1 - frames 173, 174, 175, object 2 - frames 183, 184, 185, and object 3 - frames 193, 194, 195) of three object primitives (Ontology object = Strawberry Object Primitive name = Triangle) to a video (Cherries_video.mp4).

Import Skeleton Template

# Import dependencies
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Object, ObjectInstance
from encord.objects.coordinates import SkeletonCoordinate, SkeletonCoordinates
from encord.objects.skeleton_template import SkeletonTemplate

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"  # Path to your SSH private key
PROJECT_HASH = "<unique-project-hash>"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "<name-of-data-unit>"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "<name-of-object-in-ontology>"  # Name of the object in your ontology
SKELETON_TEMPLATE_NAME = "<name-of-object-primitive>"  # Name of the skeleton template

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to label in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row
label_row.initialise_labels()

# Find a skeleton annotation object in the project ontology
skeleton_ontology_object: Object = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE, type_=Object
)
skeleton_template: SkeletonTemplate = project.ontology_structure.skeleton_templates[SKELETON_TEMPLATE_NAME]

# Instantiate an object instance from the skeleton ontology node
skeleton_object_instance: ObjectInstance = skeleton_ontology_object.create_instance()

skeleton_hashes = [coord.feature_hash for coord in skeleton_template.skeleton.values()]
skeleton_coordinates: SkeletonCoordinates = SkeletonCoordinates(values=[
    SkeletonCoordinate(
        x=0.x0, y=0.y0,  # Replace with actual coordinates
        name='point_0',
        color='#000000',
        value="point_0",
        feature_hash=skeleton_hashes[0]
    ),
    SkeletonCoordinate(
        x=0.x1, y=0.y1,  # Replace with actual coordinates
        name='point_1',
        color='#000000',
        value="point_1",
        feature_hash=skeleton_hashes[1]
    ),
    SkeletonCoordinate(
        x=0.x2, y=0.y2,  # Replace with actual coordinates
        name='point_2',
        color='#000000',
        value="point_2",
        feature_hash=skeleton_hashes[2]
    )
],
    name="<name-of-object-primitive>"  # Replace with the actual name
)

print(skeleton_coordinates)

# The x,y coordinates of the skeleton are specified as follows
skeleton_object_instance.set_for_frames(
    coordinates=skeleton_coordinates,
    # Add the skeleton to the image
    frames=0,
    # Additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the object instance to the label row
label_row.add_object_instance(skeleton_object_instance)

# Save the label row with the skeleton instance
label_row.save()
print("Label row updated with skeleton instance.")

Radio Button

Example 1:

Imports a radio button classification (Blueberry or Cherry?) to a single image (blueberry_003.jpg).

Example 2:

Imports a radio button classification (Blueberry or Cherry?) across a range of sequential frames: 193 to 197) to a video (Blueberries_video.mp4).

Radio Button Template

# Import dependencies
from __future__ import annotations
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Classification
from encord.objects.options import Option

# Configuration
SSH_PATH = "<file-path-to-ssh-private-key>"  # Path to your SSH private key
PROJECT_HASH = "<unique-project-hash-id>"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "<data-unit-name>"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "<classification-name>"  # Name of the classification in your ontology
RADIO_BUTTON_OPTION_TITLE = "<radio-button-option-title>"  # Title of the radio button option
RADIO_BUTTON_OPTION = "<radio-button-option>"  # Specify the answer for the radio button option

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to apply classification in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row 
label_row.initialise_labels()

# Find the radio classification in the project ontology
radio_ontology_classification: Classification = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE,
    type_=Classification,
)

# Find the specific radio button option
blueberry_option = radio_ontology_classification.get_child_by_title(
    title=RADIO_BUTTON_OPTION_TITLE, type_=Option
)

# Create an instance of the radio classification
radio_classification_instance = radio_ontology_classification.create_instance()

# Set the answer for the classification instance
radio_classification_instance.set_answer(
    answer=RADIO_BUTTON_OPTION
)

# Set the classification for the specified frame
radio_classification_instance.set_for_frames(
    # Add the classification to the image
    frames=0,
    # Additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the classification instance to the label row
label_row.add_classification_instance(radio_classification_instance)

# Save the label row with the classification instance
label_row.save()

print("Label row updated with classification instance.")

Checklist

Example 1:

Imports a checklist classification (Many types of fruit?) to a single image (apple_003.jpg). The selected items from the list are apple and kiwi.

Example 2:

Imports a checklist classification (Many types of fruit?) across a range of sequential frames: 193 to 197) to a video (Blueberries_video.mp4). The selected items from the list are apple and kiwi.

Checklist Template

# Import dependencies
from __future__ import annotations
from pathlib import Path
from encord import EncordUserClient, Project
from encord.objects import Classification
from encord.objects.options import Option

# Configuration
SSH_PATH = "/Users/chris-encord/sdk-ssh-private-key.txt"  # Path to your SSH private key
PROJECT_HASH = "7d4ead9c-4087-4832-a301-eb2545e7d43b"  # Unique project hash
PREDICTION_BRANCH_NAME = "<your-prediction-branch-name>"  # Specify your prediction branch
DATA_UNIT_TITLE = "Blueberries_video.mp4"  # Specific data unit title
ONTOLOGY_OBJECT_TITLE = "Many types of fruit?"  # Name of the classification in your ontology
APPLE_OPTION_TITLE = "Apple"  # Title of the apple option
KIWI_OPTION_TITLE = "Kiwi"  # Title of the kiwi option

# Create user client using access key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    Path(SSH_PATH).read_text()
)

# Get project for which predictions are to be added
project: Project = user_client.get_project(PROJECT_HASH)

# Specify the data unit to add classification in the given branch
label_row = project.list_label_rows_v2(
    branch_name=PREDICTION_BRANCH_NAME,  # Filter by prediction branch
    data_title_eq=DATA_UNIT_TITLE
)[0]

# Initialize labels for the label row
label_row.initialise_labels()

# Find the checklist classification in the project ontology
checklist_ontology_classification: Classification = project.ontology_structure.get_child_by_title(
    title=ONTOLOGY_OBJECT_TITLE,
    type_=Classification,
)

# Find the specific options in the checklist
apple_option = checklist_ontology_classification.get_child_by_title(
    title=APPLE_OPTION_TITLE, type_=Option
)

kiwi_option = checklist_ontology_classification.get_child_by_title(
    title=KIWI_OPTION_TITLE, type_=Option
)

# Create an instance of the checklist classification
checklist_classification_instance = checklist_ontology_classification.create_instance()

# Set the answers for the classification instance
checklist_classification_instance.set_answer(
    [apple_option, kiwi_option]
)

# Set the classification for the specified frame
checklist_classification_instance.set_for_frames(
    # Add the classification to the image
    frames=177,
    # Additional fields that can be set optionally:
    manual_annotation=True,
    confidence=1.0,
)

# Link the classification instance to the label row
label_row.add_classification_instance(checklist_classification_instance)

# Save the label row with the classification instance
label_row.save()

print("Label row updated with checklist classification instance.")

This simple example imports a bounding box model prediction to all data units in the prediction branch.

Store Predictions Boilerplate

# Import dependencies
import os
from encord import EncordUserClient, Project
from encord.objects import LabelRowV2, Object, OntologyStructure, ObjectInstance

from encord.objects.coordinates import BoundingBoxCoordinates, RotatableBoundingBoxCoordinates, PolygonCoordinates, PolylineCoordinates, PointCoordinate, BitmaskCoordinates


# Configuration
SSH_PATH = "file-path-to-your-ssh-key"
PROJECT_HASH = "unique-id-for-project"

# Specify a label_rows_v2 branch name for your predictions.
PREDICTION_BRANCH_NAME = "name-of-your-prediction-branch"

assert SSH_PATH is not None, "SSH path cannot be None"
assert PROJECT_HASH is not None, "Project hash cannot be None"

# Authenticate with Encord
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Access the project and prepare the branch for predictions
project = user_client.get_project(PROJECT_HASH)
prediction_branch_rows = project.list_label_rows_v2(branch_name=PREDICTION_BRANCH_NAME)

if len(prediction_branch_rows) > 0:
  print("Branch is:", prediction_branch_rows[0].branch_name)

ontology_object = project.ontology_structure.objects[0]
with project.create_bundle() as bundle:
    for row in prediction_branch_rows:
        row.initialise_labels(bundle=bundle)

for row in prediction_branch_rows:
    inst = ontology_object.create_instance()
    inst.set_for_frames(
        coordinates=BoundingBoxCoordinates(
            height=0.8,
            width=0.8,
            top_left_x=0.1,
            top_left_y=0.1,
        ),
        # Add the bounding box to the first frame
        frames=0,
        # There are multiple additional fields that can be set optionally:
        manual_annotation=False,
    )
    row.add_object_instance(inst)

with project.create_bundle() as bundle:
    for row in prediction_branch_rows:
    row.save(bundle=bundle)

Import COCO Labels as Predictions

The following code imports COCO labels as predictions for Active.

For more information on importing COCO labels into Encord, refer to our documentation.

Replace the following:

• <private_key_path> with the file path to your SSH private key.

• <my-prediction-branch-name> with the name of your prediction branch.

• <project_hash> with the Project ID for your Project.

• COCOimportfile.json with the full path of the COCO file containing the predictions you want to import.

COCO Label import as Predictions

import json
from pathlib import Path
from encord.utilities.coco.datastructure import FrameIndex
from encord import EncordUserClient
from encord.exceptions import OntologyError

# Authenticate client
SSH_PATH = "file-path-to-your-ssh-key"

# Specify a Project to import your predictions to. This Project must already exist in Encord.
PROJECT_HASH = "unique-id-for-project"

# Specify a label_rows_v2 branch name for your predictions.
PREDICTION_BRANCH_NAME = "name-of-your-prediction-branch"

# Authenticate with Encord using the path to your private key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Replace with your project hash
project = user_client.get_project(PROJECT_HASH)

# Load the COCO annotations JSON file
# Replace 'COCOimportfile.json' with the full path to your COCO file
coco_file = Path("COCOimportfile.json")
labels_dict = json.loads(coco_file.read_text())

# Build a mapping from COCO category IDs to the feature hashes in your Encord Ontology. 
category_id_to_feature_hash = {}
ont_struct = project.ontology_structure
for coco_category in labels_dict["categories"]:
    try:
        ont_obj = ont_struct.get_child_by_title(coco_category["name"])
        category_id_to_feature_hash[coco_category["id"]] = ont_obj.feature_node_hash
    except OntologyError:
        print(f"Could not match {coco_category['name']} in the Ontology. Import will crash if these are present.")

# Build a mapping from COCO image IDs to Encord frame indices
# This is only applicable for images, image groups, image sequences, and DICOM series
image_id_to_frame_index = {}
data_title_to_label_row = {lr.data_title: lr for lr in project.list_label_rows_v2()}
for img in labels_dict["images"]:
    lr = data_title_to_label_row[img["file_name"]]

    # Creates a mapping between the COCO image IDs and the corresponding frame indices in Encord
    # In this example, the target frame is 0 because the files in the sample project are single images
    image_id_to_frame_index[img["id"]] = FrameIndex(lr.data_hash, frame=0)

# Import the COCO labels into Encord
project.import_coco_labels(
    labels_dict,
    category_id_to_feature_hash,
    image_id_to_frame_index,
    branch_name=PREDICTION_BRANCH_NAME,
)

Verify Prediction Import

After importing your predictions, verify that your predictions imported.

The following code returns all labels and predictions on all branches.

# Import dependencies
from encord import EncordUserClient
import json

SSH_PATH = "file-path-of-your-ssh-key"
PROJECT_HASH = "unique-id-for-your-project"

# Instantiate client. Replace <private_key_path> with the path to the file containing your private key.
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Specify Project. Replace <project_hash> with the hash of the Project you want to export labels for.
project = user_client.get_project(PROJECT_HASH)

# Downloads a local copy of all the labels
# Without the include_all_label_branches flag only the MAIN branch labels export
label_rows = project.list_label_rows_v2(include_all_label_branches=True) 

# Initialize label rows using bundles
with project.create_bundle() as bundle:
    for label_row in label_rows:
        label_row.initialise_labels(bundle=bundle)

for label_row in label_rows:
    # Here we have the label row for the branch, but without labels themselves downloaded
    print(f"Title: {label_row.data_title}, branch: {label_row.branch_name}")

    # Print essential label information for all objects
    for object_instance in label_row.get_object_instances():
        print (f"objectHash: {object_instance.object_hash}")
        print (f"Object name: {object_instance.object_name}")
        print (f"featureHash: {object_instance.feature_hash}")
        print (f"uid: {object_instance.ontology_item.uid}")
        print (f"Object color: {object_instance.ontology_item.color}")
        print (f"Ontology shape: {object_instance.ontology_item.shape}")

        # Print the frame number and the location of the object on the frame
        for annotation in object_instance.get_annotations():
            print(f"Frame {annotation.frame} -> {annotation.coordinates}")

        # Print all attributes 
        for attribute in object_instance.ontology_item.attributes:
            print (attribute, object_instance)

    # Print all essential classification information
    for classification_instance in label_row.get_classification_instances():
        print (f"classificationHash: {classification_instance.classification_hash}")
        print (f"Classification name: {classification_instance.classification_name}")
        print (f"featureHash: {classification_instance.feature_hash}")
        print (f"Classification answer: {classification_instance.get_answer().value}")
        print (f"Classification answer hash: {classification_instance.get_answer().feature_node_hash}")


        # Print the frame number(s) that a classification appears on
        for annotation in classification_instance.get_annotations():
            print(f"Classification appears on frame: {annotation.frame}")

End-to-End Prediction Import Example

We provide an end-to-end example using a Jupyter Notebook here.

STEP 2: Import/Sync Project to Active

Import or sync the Annotate Project in Active.

STEP 3: Analyze the Predictions

Active MUST analyze the predictions before you can view the predictions in Active.

Specify whether you want to import Strict Match or Partial Match predictions.

Strict Match Predictions

All attributes are matched. This mode makes the most comprehensive comparison. A prediction is considered as a true positive only if an exact match of all attributes matches with the ground truth.

Partial Match Predictions

Only attributes provided in the prediction are compared with the ground truth. Any missing attributes in the prediction, but present in the ground truth, have no impact on the outcome of the prediction.

Perform the Analysis

DO NOT click Import on more than one prediction set at a time.

STEP 4: Select the Predictions

Once analysis completes, select the prediction set to view in Active.

Next Steps

Model and Prediction Validation

1. Prediction Metrics

2. Create Collection

3. Send to Annotate

4. Sync with Annotate

5. Update Collection

Delete Prediction Sets

You can delete prediction sets from Active from the Predictions page.