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At the moment, the function only looks in train2014. So when it gets an image_id for an image in val2014, it looks for it in train2014, and fails. (Thankfully.)
Three possible solutions:
Make split a keyword argument (to get_image_filename() first). Advantage is that this is general. Disadvantage is that the information about the split isn't present in the feature file,
where there are only three fields for each image (corpus code, image id, region id).
Make a test first, and if image_id is not in train, try in val. Advantage: API doesn't change. Disadvantage: For each image in val, there would be one unnecessary file test.
Encode information about the split in some other way, directly on the image id. E.g., 0001 is train, but 0001.5 is val. Advantage: Compatible with format of feature file. Disadvantage: turns image_id into a float, where it used to be an unsigned int.
These solutions are not mutually exclusive, at least as far as this function is concerned.
The text was updated successfully, but these errors were encountered:
The reason why this never was a problem is that all the MSCOCO dataframes we had were only for the training part. But now the cocoent data also talks about the val split.
At the moment, the function only looks in
train2014
. So when it gets an image_id for an image inval2014
, it looks for it intrain2014
, and fails. (Thankfully.)Three possible solutions:
split
a keyword argument (toget_image_filename()
first). Advantage is that this is general. Disadvantage is that the information about the split isn't present in the feature file,where there are only three fields for each image (corpus code, image id, region id).
0001
is train, but0001.5
is val. Advantage: Compatible with format of feature file. Disadvantage: turnsimage_id
into a float, where it used to be an unsigned int.These solutions are not mutually exclusive, at least as far as this function is concerned.
The text was updated successfully, but these errors were encountered: