Hello Roboflow Community,
I’m working on a project where I’m using both annotation classes and image-level tags for my images, and I need to export both types of information into a single CSV file.
My Project Setup:
- Annotation Classes: I have defined specific “Brand Names” (e.g.,
kind
,dole
,kelloggs
, etc.) as classes for my object detection annotations. When I download my dataset (e.g., asCSV
Multi Label Classification), I can successfully get thefilename
and theclass
(brand name) for each detected object in Hot encoded Format. - Image-Level Tags: Separately, I’ve used Roboflow’s image tagging feature to apply “Flavor” tags (e.g.,
chocolate
,vanilla
,strawberry
,original
) to images. These are applied at the image level in the dataset view (e.g., visible when Browse individual images or filtering).
The Issue: When I export my dataset to a CSV format (or any other format that generates a CSV-like structure), I only see the Brand Names (classes) associated with the annotations. The Flavor Tags that I’ve applied at the image level do not appear in the exported CSV.
My Desired Output Format: I would like a single CSV file that includes:
- The
filename
- The
Brand Name (Classes)
(derived from the annotation classes) - The
Flavor (Tags)
associated with that image
Ideally, something like:
FileName | Brand Name(s) | Flavor Tag(s)
image1.jpg | kind | chocolate, original
image2.jpg | kelloggs | vanilla
(Where Brand Name(s)
could be comma-separated if multiple objects of different brands are detected, and Flavor Tag(s)
could be comma-separated if an image has multiple flavor tags).
My Question: Is there a straightforward way within Roboflow to export both the annotation classes (brands) and the tags (flavors) into a single CSV? Or, what is the recommended workflow to combine these two pieces of information after export? For example, is there a separate “metadata export” for image tags that I can then merge with an annotation export?
Any guidance on how to achieve this specific data combination would be greatly appreciated.
Thank you!