Feature IC: Show how to extract DNN features for a given image(s) about computervision-recipes

[PatrickBue]

I am on the same page as you Miguel, readability over speed, and not (re)implementing our own distance metrics. That's interesting finding and somewhat surprising.

[miguelgfierro]

very nice results at the end: https://github.com/Microsoft/ComputerVision/blob/e011b08cca5eb3c35483cc1b3df8863eb51a5efe/image_similarity/notebooks/image_similarity_introduction.ipynb

there is an interesting mix of several things, first using resnet50 vs resnet18, the small one didn't converge. The key for the results I think it was to use a small feature size (512) instead of the initial ones that I had which was 2048. Not sure if also having batch normalization helped (could be, haven't tested). The small feature size probably also helps with the L2 distance. I can imagine that using KL could help if we use a larger feature size. Using finetunning vs freezing also improved the last computation.

Alexandra (what is her github user?) and I are planning to improve this, then she will take over

[ateste]

very nice results at the end: https://github.com/Microsoft/ComputerVision/blob/e011b08cca5eb3c35483cc1b3df8863eb51a5efe/image_similarity/notebooks/image_similarity_introduction.ipynb

there is an interesting mix of several things, first using resnet50 vs resnet18, the small one didn't converge. The key for the results I think it was to use a small feature size (512) instead of the initial ones that I had which was 2048. Not sure if also having batch normalization helped (could be, haven't tested). The small feature size probably also helps with the L2 distance. I can imagine that using KL could help if we use a larger feature size. Using finetunning vs freezing also improved the last computation.

Alexandra (what is her github user?) and I are planning to improve this, then she will take over

Very cool, indeed! Nice work, Miguel! My username is ateste.

[miguelgfierro]

question here @PatrickBue @loomlike @jainr @maxkazmsft

Patrick and I discussed about reformatting the computation metrics to use sklearn pairwise distances.

Recently I've been doing a lot of profiling for the reco proejct, so I did it here as well. It turns out that sklearn is much slower (I haven't tried all the functions though):

from sklearn.metrics import pairwise_distances
def compute_vector_distance2(vec1, vec2,method="l2"):
    dist = pairwise_distances(vec1.reshape(1, -1), vec2.reshape(1, -1), method)
    return dist[0][0]
print(feat1.shape) #(2048,)
%timeit compute_vector_distance(feat1, feat2, "l2")
#7.33 µs ± 43.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit compute_vector_distance2(feat1, feat2, "l2")
#109 µs ± 692 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

this happens because compute_vector_distance is using np.linalg.norm instead of the sklearn equivalent.
it's up to you guys, I'm a fan of priorizing redability over speed in python, if you think the original code is not readable I can refactor to sklearn, if you think it is readable, the original code is faster.