Hi @ramdrop , thanks for your amazing works and the release of the code. I find some positions that quite confuse me, and looking forward to your answer.
here, why the number of search is len(dbFeat), in my opinion, the n_values should be used?
faiss_index = faiss.IndexFlatL2(opt.output_dim)
faiss_index.add(dbFeat)
# dists, predictions = faiss_index.search(qFeat, max(n_values)) # the results is sorted
dists, predictions = faiss_index.search(qFeat, len(dbFeat))
I wonder why the radius dramatically change the PR performance during getting the GT?
def getPositives(self):
# positives for evaluation are those within trivial threshold range
# fit NN to find them, search by radius
if self.positives is None:
knn = NearestNeighbors(n_jobs=-1)
knn.fit(self.dbStruct.utmDb)
self.distances, self.positives = knn.radius_neighbors(self.dbStruct.utmQ, radius=self.dbStruct.nonTrivPosDistSqThr**0.5)
return self.positives
Hi @ramdrop , thanks for your amazing works and the release of the code. I find some positions that quite confuse me, and looking forward to your answer.
len(dbFeat)
, in my opinion, the n_values should be used?