Image Classification Release Notes
0.5.3
- Clean up tutorial documentation naming and remove deprecated swagger demo app
- Rework demonstration page to allow image upload and support object-based detection
0.5.2
- Clean up documentation for install and parameter descriptions
- Add documentation and functionality for environment variables in push request
0.5.1
- Update model to use single image as input type
- Update javascript demo to run with better CORS behavior (github htmlpreview)
- Additional documentation for environmental variables
- Simplify operation for active prediction to use created model (no save+load required)
0.5.0
- Documentation (lesson1) updated with model runner examples. Deprecation notice
in using explicit proto- and swagger-based serves.
- Update the structure of the protobuf input and output to use flattened (row-based)
structure instead of columnar data for all i/o channels. This should allow
other inspecting applications to more easily understand and reuse implementations
for image data.
- Update the demonstration HTML pages for similar modifications.
0.4.6
- Update image examples for open-source video.
0.4.5
- Documentation and package update to use install instructions instead of installing
this package directly into a user’s environment.
- License addition
0.4.4
- Refactor to remote the demo
bin
scripts and rewire for direct call of the
script classify_image.py
as the primary interaction mechanism.
- Refactor documentation into sections and tutorials.
- Create this release notes document for better version understanding.
0.4.3
- Minor refactor to avoid possibly reserved syntax name
0.4.2
- Refactor for compliant dataframe usage following primary client library
examples for repeated columns (e.g. dataframes) instead of custom types
that parsed rows individually.
- Refactor web, api, main model wrapper code for corresponding changes.
0.4.0
- Migration from previous library structure to new acumos client library
- Refactor to not need this library as a runtime/installed dependency
0.3
- Added example for evaluation of a multiple image with all results, saving predictions.