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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-03-11 21:30:47 -07:00
parent 2d32423461
commit 6ca8277de2
2 changed files with 2 additions and 2 deletions
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2
README.md
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@ -159,7 +159,7 @@ Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP@0.5 F1: 100%|█████| 157/157 [02:46<00:00, 1.06s/it]
all 5e+03 3.51e+04 0.822 0.433 0.611 0.551
all 5e+03 3.51e+04 0.51 0.667 0.611 0.574
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.419
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.618

2
utils/utils.py
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@ -188,7 +188,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
unique_classes = np.unique(target_cls)
# Create Precision-Recall curve and compute AP for each class
pr_score = 0.5 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
s = [len(unique_classes), tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
for ci, c in enumerate(unique_classes):