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Merged
merged 5 commits into from
Jan 23, 2025
Merged

Ch/update mes example #44

merged 5 commits into from
Jan 23, 2025

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cleherny
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@cleherny cleherny commented Jan 20, 2025

I've noticed a typo in the README of the MES example and an error occurred when running the GE swimming pool example.
For the latter, I get the following metrics:

2025-01-20 09:55:43 - INFO - Tagging detections with threshold = 0.60, which maximizes the f1-score on the val dataset.
2025-01-20 09:55:43 - INFO - Method to compute the metrics = micro-average
2025-01-20 09:55:55 - INFO - Dataset = trn => precision = 0.945, recall = 0.810, f1 = 0.872
2025-01-20 09:55:57 - INFO - Dataset = val => precision = 0.926, recall = 0.762, f1 = 0.836
2025-01-20 09:55:59 - INFO - Dataset = tst => precision = 0.910, recall = 0.782, f1 = 0.841
2025-01-20 09:56:03 - INFO - Dataset = oth => precision = 0.427, recall = 0.553, f1 = 0.482

Is this what we used to get (I don't remember)? In particular, the other metrics are much lower than the others.

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  • Coverage No coverage information (0.00% Estimated after merge)
  • Duplications 0.00% Duplicated Code (0.00% Estimated after merge)

Project ID: swiss-territorial-data-lab_object-detector_AYZ4zWIzr7JdaaSXwexc

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@GwenaelleSa GwenaelleSa left a comment

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If you tested and are confident it works properly, it's fine by me.

@@ -92,4 +92,4 @@ assess_detections.py:
output_folder: .
iou_threshold: 0.1
area_threshold: 50 # area under which the polygons are discarded from assessment
metrics_method: micro-average # 1: macro-average ; 3: macro-weighted-average ; 2: micro-average
metrics_method: macro-average # 1: macro-average ; 3: macro-weighted-average ; 2: micro-average
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It should make no difference with one class.

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Yes it is the same.

@cleherny cleherny merged commit 5773dfd into master Jan 23, 2025
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@cleherny cleherny deleted the ch/update_mes_example branch January 23, 2025 12:25
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2 participants