Hi,
I'm testing general embedding model, and my goal is to detect anomalies in series of photos.
General embedding model model returns 1024 dimensional vector with values.
Let's say I run the model with 10 really similar photos to get average values of dimensions.
Then I have the average value of each dimension (1024 values).
After that I do testing with invididual additional photos to find if photo "enough" different or not.
What kind of tool or approach would you suggest for this kind of analysis. I can do some Python and Tensorflow, but I'm wondering would excel be the easiest to visualize and detect the anomalies or is there some other tools you would suggest?
Br,
Markus.