From 8fc57a857ed53fac5d7f47ac6a2f45ad05273316 Mon Sep 17 00:00:00 2001
From: Akinmukomi Oluwaseun <sakinmukomi@seamfix.com>
Date: Wed, 29 Mar 2023 17:59:26 +0100
Subject: [PATCH] Revert "- Added code for ap50 calculation"
This reverts commit 99564e184f0206d0d994ec01e71335ee7c76756f.
---
main_lost.py | 22 ++--------------------
1 file changed, 2 insertions(+), 20 deletions(-)
diff --git a/main_lost.py b/main_lost.py
index e89e09a..5db52cf 100755
--- a/main_lost.py
+++ b/main_lost.py
@@ -334,12 +334,6 @@ if __name__ == "__main__":
if args.no_evaluation:
continue
- # Initialize variables for AP50 calculation
- tp = 0
- fp = 0
- total_gt_boxes = len(gt_bbxs)
- ap50 = 0
-
# Compare prediction to GT boxes
for pred in preds:
if len(preds) == 0:
@@ -351,31 +345,19 @@ if __name__ == "__main__":
ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(np.asarray(gt_bbxs)))
# TODO: This calculates the corloc
+ # we need to calculate the AP50
if torch.any(ious >= 0.50):
#corloc[im_id] = 1
corloc[im_id] = 0
for i in ious:
if i >= 0.50:
- corloc[im_id] += 1
-
- # Count true positives and false positives at IoU threshold of 0.5
- if torch.any(ious >= 0.50):
- tp += 1
- else:
- fp += 1
+ corloc[im_id] += 1
cnt += len(gt_bbxs)
if cnt % 50 == 0:
pbar.set_description(f"Found {int(np.sum(corloc))}/{cnt}")
- # Calculate precision and recall at IoU threshold of 0.5
- precision = tp / (tp + fp)
- recall = tp / total_gt_boxes
-
- # Calculate AP50 as average precision at IoU threshold of 0.5
- ap50 = precision * recall
- print(f"AP50: {ap50:.2f}")
# Save predicted bounding boxes
if args.save_predictions:
--
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