Examples: query, "exact match", wildcard*, wild?ard, wild*rd
Fuzzy search: cake~ (finds cakes, bake)
Term boost: "red velvet"^4, chocolate^2
Field grouping: tags:(+work -"fun-stuff")
Escaping: Escape characters +-&|!(){}[]^"~*?:\ with \, e.g. \+
Range search: properties.timestamp:[1587729413488 TO *] (inclusive), properties.title:{A TO Z}(excluding A and Z)
Combinations: chocolate AND vanilla, chocolate OR vanilla, (chocolate OR vanilla) NOT "vanilla pudding"
Field search: properties.title:"The Title" AND text
Unanswered
When It Comes To Continuous Training, I Wanted To Know How You Train Or Would Train If You Have Annotated Data Incoming? Do You Train Completely Online Where You Train As Soon As You Have A Training Example Available? Do You Instead Train When You Have A


My main query is do I wait for it to be a sufficient batch size or do I just send each image as soon as it comes to train

This is usually a cost optimization issue, generally speaking if GPU up time is not an issue that the process is stochastic anyhow, so waiting for a batch or not is not the most important factor (unless you use batchnorm layer, in that case this is basically a must)

I would not be able to split the data into train test splits, and that it would be very expensive and inefficient to train online.
...
What would be a good evaluation strategy? Splitting the batch into train test? that would mean less data for training but we can test it asap. Another idea I had was training on the current batch, then evaluating it on incoming batches. Any other ideas?

Well you could mark the new samples (50% for training, 50% for testing), then only use the testing ones (for example by rename the files or moving into a diff folder).
That said, if this is a video stream, then a sequence of frames contains very little change so splitting it to train/test basically means the test set is very very close to the train one.

  
  
Posted 2 years ago
75 Views
0 Answers
2 years ago
one year ago