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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Python Package for Airborne RGB machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 761 |
| 2026-08-05 | 762 |
| 2026-08-06 | 762 |
| 2026-08-07 | 763 |
| 2026-08-08 | 764 |
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| 2026-08-31 | 768 |
| 2026-09-02 | 769 |
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| 2026-09-06 | 771 |
| 2026-09-09 | 772 |
| 2026-09-15 | 772 |
| 2026-09-16 | 772 |
| 2026-09-17 | 773 |
| 2026-09-19 | 773 |
| 2026-09-20 | 774 |
Today
+1 stars today
This week
+2 stars this week
This month
+7 stars this month
Momentum
16.0
growth rate 0.26%/day
# DeepForest [](https://github.com/weecology/DeepForest/actions/workflows/ci_tests.yml) [](https://results.pre-commit.ci/latest/github/weecology/DeepForest/main) [](https://codecov.io/gh/weecology/DeepForest) [](http://deepforest.readthedocs.io/en/latest/?badge=latest) [](https://pypi.python.org/pypi/DeepForest) [](https://pypi.python.org/pypi/DeepForest) [](https://doi.org/10.5281/zenodo.2538143) [](https://www.python.org/downloads/) [](https://scholar.google.com/scholar?hl=en&as_sdt=40005&sciodt=0,10&cites=4018186955550406830&scipsc=&q=)   # What is DeepForest? DeepForest is a python package for training and predicting ecological objects in airborne imagery. DeepForest currently comes with a tree crown object detection model and a bird detection model. Both are single class modules that can be extended to species classification based on new data. Users can extend these models by annotating and training custom models.  # Documentation [DeepForest is documented on readthedocs](https://deepforest.readthedocs.io/) ## How does deepforest work? DeepForest uses deep learning object detection networks to predict bounding boxes corresponding to individual trees in RGB imagery. DeepForest is built on the object detection module from the [torchvision package](http://pytorch.org/vision/stable/index.html) and designed to make training models for detection simpler. For more about the motivation behind DeepForest, see some recent talks we have given on computer vision for ecology and practical applications to machine learning in environmental monitoring. ## Where can I get help, learn from others, and report bugs? Given the enormous array of forest types and image acquisition environments, it is unlikely that your image will be perfectly predicted by a prebuilt model. Below are some tips and some general guidelines to improve predictions. Get suggestions on how to improve a model by using the [discussion board](https://github.com/weecology/DeepForest/discussions). Please be aware that only feature requests or bug reports should be posted on the [issues page](https://github.com/weecology/DeepForest/issues). # Developer Guidelines If you are a new contributor to DeepForest, welcome and thank you! Here are some suggestions that we hope will make the submission process more smooth: - Look out for [good first issues](https://github.com/weecology/DeepForest/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22). These should be accessible to less experienced developers and will help you get oriented with the code. - Check that the functionality you are suggesting doesn't already exist. Perhaps it does, but needs improved documentation. - Search for issues or discussions on Github already, before submitting a PR. Do check closed issues as well. - If you are submitting a trivial improvement (like a one-line bug fix), feel free to PR directly, following the template. - If you want to propose any non-trivial changes without an existing issue please open an issue to discuss the change prior to opening a PR. Non-trivial PR's submitted without an associated issue that includes maintainer ap
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d4674c15e14f766f, llm:description: 'Python Package for Airborne RGB machine learning' (repo metadata)
matched fp:d4674c15e14f766f, llm:description: 'Python Package for Airborne RGB machine learning' (repo metadata)