different confidence threshold get the same mAP #8
Description
here is my code :
for confidence in range(1,10):
label_filename = "./evaluate/label_2"
result_path="./results/exp%d"%49
split_file = "./evaluate/lists/val.txt"
print(0.9+confidence/100)
evaluate(label_filename, result_path, split_file,score_thresh=0.9+confidence/100)
output:
0.91
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.48
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.61
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.48
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.41, 41.65
aos AP:12.04, 10.18, 10.61
0.92
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.50
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.62
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.50
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.41, 41.65
aos AP:12.04, 10.18, 10.62
0.93
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.52
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.63
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.52
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.43, 41.65
aos AP:12.04, 10.18, 10.63
0.9400000000000001
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.54
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.64
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.54
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.48, 41.65
aos AP:12.04, 10.18, 10.64
0.9500000000000001
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.58
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.66
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.58
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.53, 41.65
aos AP:12.04, 10.18, 10.66
0.96
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.62
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.67
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.62
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.59, 41.65
aos AP:12.04, 10.18, 10.67
0.97
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.69
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.92, 7.50
aos AP:12.04, 10.18, 10.70
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.69
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.67, 41.65
aos AP:12.04, 10.18, 10.70
0.98
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.75
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 7.94, 7.50
aos AP:12.04, 10.18, 10.73
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.75
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.76, 41.65
aos AP:12.04, 10.18, 10.73
0.99
Car AP(Average Precision)@0.70, 0.70, 0.70:
bbox AP:27.05, 23.71, 24.87
bev AP:35.93, 34.25, 32.38
3d AP:8.86, 8.03, 7.50
aos AP:12.04, 10.18, 10.79
Car AP(Average Precision)@0.70, 0.50, 0.50:
bbox AP:27.05, 23.71, 24.87
bev AP:61.88, 61.37, 57.36
3d AP:48.05, 43.98, 41.65
aos AP:12.04, 10.18, 10.79
Process finished with exit code 0