Trying something else?

Author: Ubuntu

nucleus/model.py

@@ -11,27 +11,24 @@
 
 Doing the three steps above allows you to visualize model performance within Nucleus, or compare multiple models that have been run on the same Dataset.
 
-Note that you can always add more predictions to a dataset, but then you will need to re-run the calculation of metrics in order to have them be correct.
-
-JUST CHECKING IF THIS RENDERS2???
-
-::
-    import nucleus
-    client = nucleus.NucleusClient("YOUR_SCALE_API_KEY")
-    prediction_1 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="1", confidence=0.9, class_pdf={'label': 0.9, 'other_label': 0.1})
-    prediction_2 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="2", confidence=0.2, class_pdf={'label': 0.2, 'other_label': 0.8})
-    model = client.add_model(name="My Model", reference_id="My-CNN", metadata={"timestamp": "121012401"})
-    # For small ingestions, we recommend synchronous ingestion
-    response = dataset.upload_predictions(model, [prediction_1, prediction_2])
-    # For large ingestions, we recommend asynchronous ingestion
-    job = dataset.upload_predictions([prediction_1, prediction_2], asynchronous=True)
-    # Check current status
-    job.status()
-    # Sleep until ingestion is done
-    job.sleep_until_complete()
-    # Check errors
-    job.errors()
-
+Note that you can always add more predictions to a dataset, but then you will need to re-run the calculation of metrics in order to have them be correct. ::
+
+
+import nucleus
+client = nucleus.NucleusClient("YOUR_SCALE_API_KEY")
+prediction_1 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="1", confidence=0.9, class_pdf={'label': 0.9, 'other_label': 0.1})
+prediction_2 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="2", confidence=0.2, class_pdf={'label': 0.2, 'other_label': 0.8})
+model = client.add_model(name="My Model", reference_id="My-CNN", metadata={"timestamp": "121012401"})
+# For small ingestions, we recommend synchronous ingestion
+response = dataset.upload_predictions(model, [prediction_1, prediction_2])
+# For large ingestions, we recommend asynchronous ingestion
+job = dataset.upload_predictions([prediction_1, prediction_2], asynchronous=True)
+# Check current status
+job.status()
+# Sleep until ingestion is done
+job.sleep_until_complete()
+# Check errors
+job.errors()
 """
 from typing import List, Optional, Dict, Union
 from .dataset import Dataset