How to Calculate Mean Average Precision from YOLO11 Detections

In this guide, we are going to walk through an example of comparing the results from two models on a single image.

First, we need to load two models. We can then run the same image through both models and retrieve their detections. We will parse the detections into an sv.Detections object. sv.Detections allows us to represent all detections in a standard way that can be processed by the supervision metrics Python API.

To load models and run inference, use the following code:

data loader.

You can load data using the following code:
‍

import supervision as sv
from supervision.metrics import MeanAveragePrecision
from inference import get_model
import matplotlib.pyplot as plt

# !wget https://media.roboflow.com/notebooks/examples/dog.jpeg
image = "dog.jpeg"

model_1 = get_model("yolo11n-640")
model_2 = get_model("yolo11s-640")

results_1 = model_1.infer(image)[0]
results_2 = model_2.infer(image)[0]
  
detections_1 = sv.Detections.from_inference(results_1)
detections_2 = sv.Detections.from_inference(results_2)

We now have predictions from two models from which we can calculate metrics.

We can calculate

Mean Average Precision (mAP)

using detections from two or more 

YOLO11

models (or, compare ground truth annotations to the results from a model) using the supervision metrics API. This API has support for calculating several metrics for populat computer vision models.

We can calculate 

Mean Average Precision (mAP)

using the following code:
‍

# MeanAveragePrecision().update([detections], [targets])
# in this example, we assume detections_2 contains the best detections (the largest model)
# if you are using the API with a ground truth dataset, detections_2 could be annotations from your dataset
# learn how to load annotations from a dataset with https://supervision.roboflow.com/latest/datasets/core/
map_n_metric = MeanAveragePrecision().update([detections_1], [detections_2]).compute()
print(map_n_metric.map50_95)

from supervision.metrics import F1Score

# .update() takes detections and targets
# in this example, we assume detections_2 contains the best detections (the largest model)
# if you are using the API with a ground truth dataset, detections_2 could be annotations from your dataset
# learn how to load annotations from a dataset with https://supervision.roboflow.com/latest/datasets/core/
f1_metric = F1Score()
f1_result = f1_metric.update(detections_1, detections_2).compute()

print(f1_result)
print(f1_result.f1_50)
print(f1_result.small_objects.f1_50)