Multiclass classification models predict the single best fitting category
for an input image among a predefined set of possibilities.
The set of possible categories can be ordered (e.g. ["bad", "ok", "good"]), in
which case the task could be called "ordinal classification".
Datasets follow this structure:
endpoint_url/bucket
├── prefix/images/
├── prefix/annotations.yaml
└── prefix/metadata.yaml
Dataset images are placed directly inside images/ (subdirectories are ignored).
The metadata file looks something like
this:
task: multiclass classification
annotations: annotations.yaml
categories: [cat1, cat2, cat3]
The annotations field specifies the name of
the file containing the ground truth annotations.
Here's an example of
annotations file:
000.jpg: cat2
001.jpg: cat3
002.jpg: cat1
# ...