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Core Compatibility Report: polite-pig/v0¤

✔️ bioimageio format validation
status passed
source https://hypha.aicell.io/bioimage-io/artifacts/polite-pig/files/rdf.yaml?version=v0
id polite-pig
version 1.1
applied format model 0.5.9
bioimageio.spec 0.5.9.1
Location Details
✔️ Successfully created `ModelDescr` instance.
✔️ bioimageio.spec format validation model 0.5.9
documentation
No '# Validation' (sub)section found in README.md.
inputs.0.sample_tensor
Needs to be filled for FAIR compliance
outputs.0.sample_tensor
Needs to be filled for FAIR compliance
outputs.1.sample_tensor
Needs to be filled for FAIR compliance
✔️ weights.onnx Created conda environment '4d82a6bc4511b4ad0459f190edebc373e2ca235c8452606867e45b1507100a34'
✔️ weights.onnx Reproduce test outputs from test inputs (onnx)
weights.onnx
Output `classes`: all elements agree with expected values. 
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|3.18e-01 - 3.18e-01\|/\|3.18e-01 + 1e-6\|) at {'batch': np.int64(0), 'channel': np.int64(0)} 
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|2.9347383e-04 - 2.9359723e-04\|) at {'batch': np.int64(0), 'channel': np.int64(8)}
weights.onnx
Output `features`: all elements agree with expected values. 
Max relative difference not accounted for by absolute tolerance (1.00e-03):
0.00e+00 (= \|1.52e-01 - 1.52e-01\|/\|1.52e-01 + 1e-6\|) at {'batch': np.int64(0), 'channel': np.int64(0)} 
Max absolute difference not accounted for by relative tolerance (1.00e-03):
0.00e+00 (= \|2.2174418e-04 - 2.2174467e-04\|) at {'batch': np.int64(0), 'channel': np.int64(341)}
weights.onnx
recommended conda environment (Reproduce test outputs from test inputs (onnx))
%YAML 1.2
---
channels:
  - conda-forge
  - nodefaults
dependencies:
  - conda-forge::bioimageio.core>=0.9.4
  - onnxruntime
  - pip
weights.onnx
conda compare (Reproduce test outputs from test inputs (onnx))
Success. All the packages in the specification file are present in the environment with matching
version and build string.
✔️ weights.onnx Run onnx inference for inputs with batch_size: 1 and size parameter n: 0