A python library for Object Detection metrics.
- User-friendly: simple to set and simple to use;
- Highly Customizable: every parameters that occur in the definition of
mAPandmARcan be set by user to custom values; - Compatibility with COCOAPI: each calculated metric is tested to coincide with COCOAPI metrics.
Supported metrics include mAP (Mean Average Precision), mAR (Mean Average Recall)
and IoU (Intersection over Union).
For help, usage and API reference, please refer to Documentation
Try OD-Metrics samples
Install from PyPI
pip install od-metrics
Install from Github
pip install git+https://github.com/EMalagoli92/OD-Metrics
from od_metrics import ODMetrics
# Ground truths
y_true = [
{ # image 1
"boxes": [[25, 16, 38, 56], [129, 123, 41, 62]],
"labels": [0, 1]
},
{ # image 2
"boxes": [[123, 11, 43, 55], [38, 132, 59, 45]],
"labels": [0, 0]
}
]
# Predictions
y_pred = [
{ # image 1
"boxes": [[25, 27, 37, 54], [119, 111, 40, 67], [124, 9, 49, 67]],
"labels": [0, 1, 1],
"scores": [.88, .70, .80]
},
{ # image 2
"boxes": [[64, 111, 64, 58], [26, 140, 60, 47], [19, 18, 43, 35]],
"labels": [0, 1, 0],
"scores": [.71, .54, .74]
}
]
metrics = ODMetrics()
output = metrics.compute(y_true, y_pred)
print(output)
"""
{'mAP@[.5 | all | 100]': 0.2574257425742574,
'mAP@[.5:.95 | all | 100]': 0.10297029702970294,
'mAP@[.5:.95 | large | 100]': -1.0,
'mAP@[.5:.95 | medium | 100]': 0.10297029702970294,
'mAP@[.5:.95 | small | 100]': -1.0,
'mAP@[.75 | all | 100]': 0.0,
'mAR@[.5 | all | 100]': 0.25,
'mAR@[.5:.95 | all | 100]': 0.1,
'mAR@[.5:.95 | all | 10]': 0.1,
'mAR@[.5:.95 | all | 1]': 0.1,
'mAR@[.5:.95 | large | 100]': -1.0,
'mAR@[.5:.95 | medium | 100]': 0.1,
'mAR@[.5:.95 | small | 100]': -1.0,
'mAR@[.75 | all | 100]': 0.0,
'classes': [0, 1],
'n_images': 2}
"""This work is made available under the MIT License