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Co-authored-by: Rowan Cockett <[email protected]>

Author: jayqi

papers/drivendata/main.md

@@ -31,6 +31,7 @@ Machine learning (ML) models have shown significant promise in automating specie
 
 Accurate, accessible, and automated species detection enables improved monitoring of animal populations and evaluation of conservation impacts on species abundance. Faster processing of camera trap data allows conservationists to conduct these assessments in weeks instead of years. This not only supports timely evaluations and interventions—allowing for adaptive management if efforts are not proving effective—but also enables the collection of more data from a broader range of locations. It also allows conservationists to direct their time toward more complex secondary analyses and making evidence-based conservation decisions.
 
+(development-history)=
 ## Development history
 
 Zamba's development has its origins in online machine learning challenges hosted by DrivenData in partnership with wildlife conservation experts—in particular from the Max Planck Institute for Evolutionary Anthropology (MPI-EVA). Machine learning competitions have proven effective at harnessing community-driven innovation and enabling the rapid testing of thousands of approaches to solving a problem [@doi:10.48550/arXiv.1606.07781]. Zamba, named after the word for “forest” in Lingala[^footnote-lingala], was created to make these advances more accessible to ecologists and conservationists.
@@ -237,6 +238,7 @@ The `slowfast` model architecture uses the SlowFast [@doi:10.48550/arXiv.1812.03
 | `european` | Western Europe | Trained on non-jungle ecologies | Finetuned `time_distributed` model | \~13,000 |
 :::
 
+(frame-selection-approach)=
 #### Frame selection approach
 
 One of the key technical challenges in working with camera trap videos—rather than still images—is frame selection. For datasets that motivated the development of Zamba, videos had a frame rate of 30 frames per second and were typically about 60 seconds long. Processing every frame as an image for such videos is computationally infeasible. Furthermore, animals may only be present in a minority of recorded frames, and selecting frames without the animal degrades the performance of downstream tasks like species classification and depth estimation.
@@ -318,6 +320,7 @@ Blank detection is an important feature in camera trap workflows due to the high
 
 While Zamba's pretrained models are trained to predict specific classes for species from Africa and Western Europe, it can be possible to effectively apply them to out-of-domain data that includes geographies or species that the model has never seen. In @takahe_case_study, we show a case study using a dataset of 3,056 videos from New Zealand. Using the pretrained African species `time_distributed` model, the `bird` label correctly captures 77% of the takahē videos (recall) at 82% precision, which is particuarly notable since the takahē is a New-Zealand-native flightless bird that is not present in the African training data. However, other New Zealand-native birds like the weka are less successfully captured by the `bird` category (44% recall). [Training custom models](#custom-model-training) is recommended to achieve more accurate classification on new geographies and species.
 
+(configuration-and-customization)=
 #### Configuration and customization
 
 All Zamba workflows—inference, fine-tuning, and training—support an extensive range of configuration options. For instance, processed videos are resized to configurable size, by default 240x426 pixels. Higher resolution videos will lead to superior accuracy in prediction, but will use more memory and take longer to train or predict.
@@ -333,6 +336,7 @@ The number of frames selected is also customizable (default: 16).
 
 Configurations can be provided via command-line flags or through YAML-formatted configuration files.
 
+(custom-model-training)=
 #### Custom model training
 
 One of Zamba’s signature features is its support for training custom models. Custom models are primarily built by fine-tuning general-purpose species classifiers and can enhance performance in specific ecological contexts—even when classifying the same species. Custom models are also essential for extending classification to new species, new habitats, or label classes at different taxonomic ranks.