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Update the bird model #1247

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80467eb
Update bird detector
bw4sz Apr 21, 2026
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Remove trailing whitespace in 02_prebuilt.md
bw4sz Apr 21, 2026
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30 changes: 7 additions & 23 deletions docs/user_guide/02_prebuilt.md
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Expand Up @@ -34,6 +34,13 @@ The model was initially described in [Ecological Applications](https://esajourna
Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources.
>

The bird detection model has been updated and retrained from the original `weecology/deepforest-bird` model. The updated model was fine-tuned starting from the tree detection model (`weecology/deepforest-tree`) and trained on data from both Weinstein et al. 2022 as well as new additional bird detection data from multiple sources including https://lila.science/. The result is a dataset with over a million bird detections from around the world. Training details and metrics can be viewed on the [Comet dashboard](https://www.comet.com/bw4sz/bird-detector/6181df1ab7ac40f291b863a2a9b86024?&prevPath=%2Fbw4sz%2Fbird-detector%2Fview%2Fnew%2Fexperiments).

### Example Predictions

The following examples show predictions from the updated bird detection model:

![Bird Prediction Example 1](../figures/bird_prediction_example_1.png)

### Citation
> Weinstein, B.G., Garner, L., Saccomanno, V.R., Steinkraus, A., Ortega, A., Brush, K., Yenni, G., McKellar, A.E., Converse, R., Lippitt, C.D., Wegmann, A., Holmes, N.D., Edney, A.J., Hart, T., Jessopp, M.J., Clarke, R.H., Marchowski, D., Senyondo, H., Dotson, R., White, E.P., Frederick, P. and Ernest, S.K.M. (2022), A general deep learning model for bird detection in high resolution airborne imagery. Ecological Applications. Accepted Author Manuscript e2694. https://doi-org.lp.hscl.ufl.edu/10.1002/eap.2694
Expand Down Expand Up @@ -122,29 +129,6 @@ Table S1 Confusion matrix for the Alive/Dead model in Weinstein et al. 2023

Citation: Weinstein, Ben G., et al. "Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model." Remote Sensing in Ecology and Conservation 9.5 (2023): 656-670.

### NEON Tree Species and Genus Classification

Two ResNet-18 crop classifiers trained on RGB crown images from the National Ecological Observatory Network (NEON). The training data includes deduplicated hand-annotated tree crowns from 29 NEON sites across the US.

- **Species model**: 148 species classes, trained on ~16k deduplicated crown crops. HuggingFace repo: `weecology/cropmodel-tree-species`
- **Genus model**: 54 genus classes, same training data aggregated to genus level. HuggingFace repo: `weecology/cropmodel-tree-genus`

Both models use a torchvision ResNet-18 backbone pretrained on ImageNet and fine-tuned on NEON RGB data. Input images are resized to 224x224 using nearest-neighbor interpolation (`resize_interpolation: nearest` in the model config) and normalized with standard ImageNet statistics. The interpolation mode is loaded automatically from the HuggingFace config — no user action required.

```python
from deepforest.model import CropModel

# Load the species classifier
species_model = CropModel.load_model("weecology/cropmodel-tree-species")

# Load the genus classifier
genus_model = CropModel.load_model("weecology/cropmodel-tree-genus")
```

Use these as a second stage after tree crown detection: detect crowns with a DeepForest model, then classify each crop.

For more details on the training data and code, see [NeonTreeClassification](https://github.com/GatorSense/NeonTreeClassification).

## Want more pretrained models?

Please consider contributing your data to open source repositories, such as zenodo or lila.science. The more data we gather, the more we can combine the annotation and data collection efforts of hundreds of researchers to built models available to everyone. We welcome suggestions on what models and data are most urgently [needed](https://github.com/weecology/DeepForest/discussions).
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