library("rplatypus")
available_weights()
spec <- platypus_spec(
data = segmentation_data("images/", "images/", colormap = binary_colormap),
models = list(u_net("nuclei", input_shape = c(256, 256), blocks = 4, filters = 16,
weights = "dsbowl-unet", fit = FALSE))
)
masks <- predict(platypus_fit(spec), split = "validation")The published weights, and what they are
Description
Weights are named rather than downloaded by hand, and a name is pinned to one commit of the repository holding it - so “dsbowl-unet” means one set of numbers today and the same set next year. This lists what is published, with the commit each name resolves to.
Usage
available_weights()
Details
Read the descriptions rather than only the names. A model’s limitations decide whether it answers your question: the nuclei weights are semantic, so touching nuclei come back as one region and a count taken from them would be wrong.
Value
A data frame with name, repo, filename, revision and description.
See Also
u_net() and the other architectures, whose weights argument takes a name from here, a hf://owner/repo/file.safetensors@commit reference, or a path to a local file.