Key Version 2 Updates¶

Released:

Previous Model - Machine Learning Competition¶

The algorithms used by zamba v1 were based on the winning solution from the Pri-matrix Factorization machine learning competition, hosted by DrivenData. Data for the competition was provided by the Chimp&See project and manually labeled by volunteers. The competition had over 300 participants and over 450 submissions throughout the three month challenge. The v1 algorithm was adapted from the winning competition submission, with some aspects changed during development to improve performance.

The core algorithm in zamba v1 was a stacked ensemble which consisted of a first layer of models that were then combined into a final prediction in a second layer. The first level of the stack consisted of 5 keras deep learning models, whose individual predictions were combined in the second level of the stack to form the final prediction.

In v2, the stacked ensemble algorithm from v1 is replaced with three more powerful single-model options: time_distributed, slowfast, and european. The new models utilize state-of-the-art image and video classification architectures, and are able to outperform the much more computationally intensive stacked ensemble model.

New geographies and species¶

zamba v2 incorporates data from western Europe (Germany) in additional to locations in central and west Africa. The new data is packaged in the pretrained european model, which can predict 11 common European species not present in zamba v1.

zamba v2 also incorporates new training data for central and west Africa. zamba v1 was primarily focused on species commonly found on savannas. v2 incorporates data from camera traps in jungle ecosystems, adding 13 additional species to the pretrained models for central and west Africa.

Retraining flexibility¶

Model training is easier to reproduce in zamba v2, so users can finetune a pretrained model using their own data. zamba v2 also allows users to retrain a model on completely new labels.