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?