release v2.3.0

drivendataorg · Dec 1, 2022 · 386e0e0 · 386e0e0
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diff --git a/HISTORY.md b/HISTORY.md
@@ -1,5 +1,11 @@
 # `zamba` changelog
 
+## v2.3.0 (2022-12-01)
+
+### Model release
+
+* Adds a depth estimation module for predicting the distance between animals and the camera ([PR #247](https://github.com/drivendataorg/zamba/pull/247)). This model comes from one of the winning solutions in the [Deep Chimpact: Depth Estimation for Wildlife Conservation](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/) machine learning challenge hosted by DrivenData.
+
 ## v2.2.4 (2022-11-10)
 
 * Do not cache videos if the `VIDEO_CACHE_DIR` environment variable is an empty string or zero ([PR #245](https://github.com/drivendataorg/zamba/pull/245))

diff --git a/docs/docs/changelog/index.md b/docs/docs/changelog/index.md
@@ -1,5 +1,11 @@
 # `zamba` changelog
 
+## v2.3.0 (2022-12-01)
+
+### Model release
+
+* Adds a depth estimation module for predicting the distance between animals and the camera ([PR #247](https://github.com/drivendataorg/zamba/pull/247)). This model comes from one of the winning solutions in the [Deep Chimpact: Depth Estimation for Wildlife Conservation](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/) machine learning challenge hosted by DrivenData.
+
 ## v2.2.4 (2022-11-10)
 
 * Do not cache videos if the `VIDEO_CACHE_DIR` environment variable is an empty string or zero ([PR #245](https://github.com/drivendataorg/zamba/pull/245))

diff --git a/docs/docs/models/depth.md b/docs/docs/models/depth.md
@@ -2,7 +2,7 @@
 
 ## Background
 
-Our depth estimation model is useful for predicting the distance an animal is from the camera, which is an input into models used to estimate animal abundance. 
+Our depth estimation model is useful for predicting the distance an animal is from the camera, which is an input into models used to estimate animal abundance.
 
 The depth model comes from one of the winners of the [Deep Chimpact: Depth Estimation for Wildlife Conservation](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/) machine learning challenge hosted by DrivenData. The goal of this challenge was to use machine learning and advances in monocular (single-lens) depth estimation techniques to automatically estimate the distance between a camera trap and an animal contained in its video footage. The challenge drew on a unique labeled dataset from research teams from the Max Planck Institute for Evolutionary Anthropology (MPI-EVA) and the Wild Chimpanzee Foundation (WCF).
 

diff --git a/setup.cfg b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = zamba
-version = 2.2.4
+version = 2.3.0
 author = DrivenData
 author_email = info@drivendata.org
 description = Zamba is a command line tool and Python package to identify animals in camera trap videos and train custom models for new species and habitats.