3. Evaluate a Detection Model

1. Context

A. Pre-requisite

  • A Projecton Picsellia allowing you to host Experiment and DatasetVersion.
  • A DatasetVersionconfigured in OBJECT_DETECTION and annotated.
  • Have an Experimentwith a DatasetVersion attached to it - you can add the test alias to this DatasetVersion.
  • A Object Detection local model or a Object DetectionModelVersion.


If you want to integrate your local custom object detection model into Picsellia you can checkout this tutorial πŸ‘‰ Migrate your Models to Picsellia

B. Variables

Let's say that:

  • The Project is called Documentation Project
  • The Experimentis called my_experiment
  • The DatasetVersionattached is called test

C. Setup

You need to have a post-processing function that will return the predicted Label and it's confidence_treshold and boxes formatted in denormalized x, y, w, h.

def predict(input: Image, model: TF.Model/PT.Model):
    preprocessed_input = pre_process(input)
    prediction = model(preprocessed_input)
    class_name, confidence_treshold, denormalized_boxes = post_process(prediction) 
    return class_name, confidence_treshold, denormalized_boxes

You should also create a script that will initialise picsellia Clientconnexion and fetch your Project,Experiment, DatasetVersion.

from picsellia import Client 

client = Client(api_token, organization_name, host='https://app.picsellia.com')

project = client.get_project(name='Documentation Project')
experiment = project.get_experiment(name='my_experiment')

testing_dataset = experiment.get_dataset('test')

We also need to create a dictionary matching class_names and the Label objects from Picsellia in order to attach the good Label. Something like that:

  "cat": PicselliaLabel(Python Object),
  "dog": PicselliaLabel(Python Object)
picsellia_labels_name = testing_dataset.list_labels()

label_matching = {k.name: k for k in picsellia_labels_name}

2. Implementing the Model Testing

Let's take a look at the Experiment add_evaluation() method:

   asset: Asset, add_type: Union[str,
   AddEvaluationType] = AddEvaluationType.REPLACE,
   rectangles: Optional[List[Tuple[int, int, int, int, Label, float]]] = None,
   polygons: Optional[List[Tuple[List[List[int]], Label, float]]] = None,
   classifications: Optional[List[Tuple[Label, float]]] = None

Let's dive into 3 of the arguments:

  • asset: Asset (Meaning that you can only have one evaluation by Asset)
  • add_type: It's an enum with these possibilities : (KEEP/REPLACE) the default is REPLACE. KEEP will keep the existing evaluation if it exists.
  • rectangles: it's a list of Tuple, the Tuple being ([x, y, w, h], Label, confidence_score)

Let's wrap everything together with a YOLOv8 detector from Ultralytics, here is the snippet from HuggingFace:

from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.boxes)  # print the Boxes object containing the detection bounding boxes

Let's format this in order to integrate Picsellia into this:

import numpy as np
from picsellia import Client
from picsellia.sdk.asset import Asset
from picsellia.types.enums import InferenceType
from ultralytics import YOLO

client = Client(api_token="", organization_name="")
project = client.get_project(name='Documentation Project')
experiment = project.get_experiment(name='my_experiment')
testing_dataset = experiment.get_dataset('test')
picsellia_labels_name = dataset.list_labels()
label_matching = {k.name: k for k in picsellia_labels_name}

model = model = YOLO('yolov8n.pt')

def postprocess(results):
    r = results[0].cpu()
    confs = r.boxes.conf.numpy().astype(np.float)
    boxes = r.boxes.xywh.numpy().astype(int)
    classes_index = r.boxes.cls.numpy().astype(int)
    return classes_index, boxes, confs
for asset in dataset.list_assets():
    results = model(
    classes_idx, boxes, confs = postprocess(results)
    evaluated_rectangles = []
    for idx, box, conf in list(zip(classes_idx, boxes, confs)):
        cls_name = model.names[idx]
        evaluated_rectangles.append((box, label_matching[cls_name], conf))
    experiment.add_evaluation(asset, rectangles=evaluated_rectangles)