I need to calculate the mAP described in this question for object detection using Tensorflow.
Average precision(AP) is a typical performance measure used for ranked sets. AveragePrecision is defined as the average of the precision scores after each true positive, TP in the scope S. Given a scope S = 7,and a ranked list (gain vector) G = [1,1,0,1,1,0,0,1,1,0,1,0,0,..] where 1/0 indicate the gains associated to relevant/non-‐relevant items, respectively:
AP = (1/1 + 2/2 + 3/4 + 4/5) / 4 = 0.8875.
Mean Average Precision (mAP): average of the average precision value for a set of queries.
i got 5 One-Hot tensors with the predictions:
prediction_A
prediction_B
prediction_C
prediction_D
prediction_E
where a single prediction tensor has this structure (for example prediction_A):
00100
01000
00001
00010
00010
Then i've got the correct labels (one-hot) tensors, with the same structure:
y_A
y_B
y_C
y_D
y_E
i want compute mAP using tensorflow, cause i want summarize that, how i can do it?
i found this function but i can't use it, cause i have a multidimensional vector.
I also write a python function that compute AP but it doesn't use Tensorflow
def compute_av_precision(match_list):
n = len(match_list)
tp_counter = 0
cumulate_precision = 0
for i in range(0,n):
if match_list[i] == True:
tp_counter += 1
cumulate_precision += (float(tp_counter)/float(i+1))
if tp_counter != 0:
av_precision = cumulate_precision/float(tp_counter)
return av_precision
return 0
I think you may need this one:
tf.metrics.average_precision_at_k
this method takes labels and prediction to calcualte the AP@K you mentioned
below are the referenced links
https://www.tensorflow.org/api_docs/python/tf/metrics/average_precision_at_k
which implemented AP@K metric defined here:
https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
BTW, if you need a metric in Tensorflow, firstly you should search inside their official documents. Here is a list of all implemented metrics
cheers