Thanks for your work! It's really handy, saving me a lot of time when learning YOLO in keras :)
I understand this program is for python 3. I run it with python 2.7 though, and a small modification will get it work.
In File "mean_average_precision/mean_average_precision/utils/bbox.py", function jaccard returns 0 when the coordinates of bounding boxes are all integers, because python 2.7 yields an integer for integer division. Modifying line 49 from
return inter / union
to
return inter.astype('float32') / union
works well.
Second, when calculating mAP for just one class, in File "mean_average_precision/mean_average_precision/detection_map.py", function plot raises an error
Traceback (most recent call last):
File "mean_average_precision/detection_map.py", line 229, in plot
for i, ax in enumerate(axes.flat):
AttributeError: 'AxesSubplot' object has no attribute 'flat'
because when grid == 1, fig, axes = plt.subplots(nrows=grid, ncols=grid) return axes as type of matplotlib.axes._subplots.AxesSubplot.
I modify it from
grid = int(math.ceil(math.sqrt(self.n_class)))
fig, axes = plt.subplots(nrows=grid, ncols=grid)
mean_average_precision = []
# TODO: data structure not optimal for this operation...
for i, ax in enumerate(axes.flat):
if i > self.n_class - 1:
break
precisions, recalls = self.compute_precision_recall_(i, interpolated)
average_precision = self.compute_ap(precisions, recalls)
self.plot_pr(ax, i, precisions, recalls, average_precision)
mean_average_precision.append(average_precision)
to
grid = int(math.ceil(math.sqrt(self.n_class)))
if grid == 1:
fig, ax = plt.subplots()
mean_average_precision = []
precisions, recalls = self.compute_precision_recall_(0, interpolated)
average_precision = self.compute_ap(precisions, recalls)
self.plot_pr(ax, 0, precisions, recalls, average_precision)
mean_average_precision.append(average_precision)
else:
fig, axes = plt.subplots(nrows=grid, ncols=grid)
mean_average_precision = []
# TODO: data structure not optimal for this operation...
for i, ax in enumerate(axes.flat):
if i > self.n_class - 1:
break
precisions, recalls = self.compute_precision_recall_(i, interpolated)
average_precision = self.compute_ap(precisions, recalls)
self.plot_pr(ax, i, precisions, recalls, average_precision)
mean_average_precision.append(average_precision)
to get it work, but I think it can be more precise .
Thanks for your work! It's really handy, saving me a lot of time when learning YOLO in keras :)
I understand this program is for python 3. I run it with python 2.7 though, and a small modification will get it work.
In File "mean_average_precision/mean_average_precision/utils/bbox.py", function jaccard returns 0 when the coordinates of bounding boxes are all integers, because python 2.7 yields an integer for integer division. Modifying line 49 from
return inter / unionto
return inter.astype('float32') / unionworks well.
Second, when calculating mAP for just one class, in File "mean_average_precision/mean_average_precision/detection_map.py", function plot raises an error
because when grid == 1,
fig, axes = plt.subplots(nrows=grid, ncols=grid)return axes as type ofmatplotlib.axes._subplots.AxesSubplot.I modify it from
to
to get it work, but I think it can be more precise .