Skip to content

Latest commit

 

History

History
38 lines (32 loc) · 2.08 KB

CHANGELOG.rst

File metadata and controls

38 lines (32 loc) · 2.08 KB

Changelog

Version 0.1.17

  • [BREAKING CHANGE] Added detection label mapping for the phase1 output to rename the following species labels to be consistent with the mvp output labels:

    • elephant_savanna to elephant
    • dead_animalwhite_bones to white_bones
    • deadbones to white_bones
    • elecarcass_old to white_bones
    • gazelle_gr to gazelle_grants
    • gazelle_th to gazelle_thomsons
  • Added rounding to the WIC predicted confidence to 4 decimal points in the print and JSON outputs.

  • Added to the documentation the list of supported class labels for each model configuration.

  • Updated the documentation to highlight the list of recommended supported species for the MVP model (based on held-out validation data), and to add the build and deployment instructions.

  • Added platform detection code to detect macOS and reduce the batch size of WIC models with the MVP model to 1 (added to Known Issues).

  • Added three new environment variables to allow specifying the model configuration for the WIC, LOC, and AGG, respectively: WIC_CONFIG, LOC_CONFIG, AGG_CONFIG. If unset, it uses the global config and behavior as specified by the CONFIG environment variable. The TILE module does not have different settings dependent on the model configuration.

  • Added a new environment variable to allow for faster but less accurate results: FAST. If unset, it uses the standard tile extraction behavior for grid1 and grid2. Turning this flag on will dramatically speed up inference by processing approximately half of the number of tiles per image.

  • Added CHANGELOG.rst and ISSUES.rst.

  • Modified documentation strings in a few places for clarity and correctness.

  • Updated both Gradio apps to support the Phase1 and MVP models and their respective configurations.

  • Updated tests to support the new ML configuration default settings.

Version 0.1.16

Alpha version of Scoutbot, with all Phase 1 and MVP functionality and pre-trained models included