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https://github.com/microsoft/CameraTraps

PyTorch Wildlife: a Collaborative Deep Learning Framework for Conservation.
https://github.com/microsoft/CameraTraps

camera-traps computer-vision conservation machine-learning megadetector pytorch pytorch-wildlife wildlife

Last synced: 11 months ago
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PyTorch Wildlife: a Collaborative Deep Learning Framework for Conservation.

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README 

          
![image](https://zenodo.org/records/15376499/files/Pytorch_Banner_transparentbk.png)



 

 A Collaborative Deep Learning Framework for Conservation 






 

 

 



















## 📣 Announcements



### V 1.2.3



- We have fully recreated our [documentation page](https://microsoft.github.io/CameraTraps/) with [MKDocs](https://squidfunk.github.io/mkdocs-material/). Please take a look and let us know what you think! (Special thanks to @ss26 for creating the foundation of this documentation page!)  



- We have released new MegaDetector V6 model weights. MDV6-yolov9-e and MDV6-yolov10-e are now retrained at 1280 input resolution to match the original V5 setup. In addition, we have added both compact and extra versions for [YOLOv9 MIT](https://github.com/MultimediaTechLab/YOLO?tab=readme-ov-file) and [RT-DETR Apache](https://github.com/lyuwenyu/RT-DETR/tree/main) implementations, expanding the range of V6 options for different deployment needs.



- Our [Model Zoo](https://microsoft.github.io/CameraTraps/model_zoo/megadetector/) now includes new performance numbers evaluated using [pycocotools](https://pypi.org/project/pycocotools/), enabling easier and more standardized comparisons with MIT and Apache models. All metrics are reported on 1280-sized input images to ensure consistency across versions. While V5 and V6 now deliver comparable performance, V6 models are still significantly smaller. Model weights for all versions, including MIT and Apache variants, are available for download, and we’ll be releasing the inference code for MIT and Apache models soon.



- To further improve V6 model generalization performance, our next iteration of V6 training will incorporate some new public camera trap datasets from LILA, including [Orinoquía Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/) and other datasets that have bounding boxes and are currently not in V6's training pool.



#### Previous versions:

- [What's New](https://microsoft.github.io/CameraTraps/releases/release_notes/)



## 👋 Welcome to Pytorch-Wildlife



**PyTorch-Wildlife** is an AI platform designed for the AI for Conservation community to create, modify, and share powerful AI conservation models. It allows users to directly load a variety of models including [MegaDetector](https://microsoft.github.io/CameraTraps/megadetector/), [DeepFaune](https://microsoft.github.io/CameraTraps/megadetector/), and [HerdNet](https://github.com/Alexandre-Delplanque/HerdNet) from our ever expanding [model zoo](https://microsoft.github.io/CameraTraps/model_zoo/megadetector/) for both animal detection and classification. In the future, we will also include models that can be used for applications, including underwater images and bioacoustics. We want to provide a unified and straightforward experience for both practicioners and developers in the AI for conservation field. Your engagement with our work is greatly appreciated, and we eagerly await any feedback you may have.



Explore the codebase, functionalities and user interfaces of **Pytorch-Wildlife** through our [documentation](https://microsoft.github.io/CameraTraps/), interactive [HuggingFace web app](https://huggingface.co/spaces/AndresHdzC/pytorch-wildlife) or local [demos and notebooks](./demo). 



## 🚀 Quick Start



👇 Here is a quick example on how to perform detection and classification on a single image using `PyTorch-wildlife`

```python

import numpy as np

from PytorchWildlife.models import detection as pw_detection

from PytorchWildlife.models import classification as pw_classification



img = np.random.randn(3, 1280, 1280)



# Detection

detection_model = pw_detection.MegaDetectorV6() # Model weights are automatically downloaded.

detection_result = detection_model.single_image_detection(img)



#Classification

classification_model = pw_classification.AI4GAmazonRainforest() # Model weights are automatically downloaded.

classification_results = classification_model.single_image_classification(img)

```

More models can be found in our [model zoo](https://microsoft.github.io/CameraTraps/model_zoo/megadetector/)



## ⚙️ Install Pytorch-Wildlife

```

pip install PytorchWildlife

```

Please refer to our [installation guide](https://microsoft.github.io/CameraTraps/installation/) for more installation information.



## 📃 Documentation

Please also go to our newly made dofumentation page for more information: [![](https://img.shields.io/badge/Docs-526CFE?logo=MaterialForMkDocs&logoColor=white)](https://microsoft.github.io/CameraTraps/)



## 🖼️ Examples



### Image detection using `MegaDetector`

animal_det_1


*Credits to Universidad de los Andes, Colombia.*



### Image classification with `MegaDetector` and `AI4GAmazonRainforest`

animal_clas_1


*Credits to Universidad de los Andes, Colombia.*



### Opossum ID with `MegaDetector` and `AI4GOpossum`

opossum_det


*Credits to the Agency for Regulation and Control of Biosecurity and Quarantine for Galápagos (ABG), Ecuador.*



## :fountain_pen: Cite us!

We have recently published a [summary paper on Pytorch-Wildlife](https://arxiv.org/abs/2405.12930). The paper has been accepted as an oral presentation at the [CV4Animals workshop](https://www.cv4animals.com/) at this CVPR 2024. Please feel free to cite us!



```

@misc{hernandez2024pytorchwildlife,

      title={Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation}, 

      author={Andres Hernandez and Zhongqi Miao and Luisa Vargas and Sara Beery and Rahul Dodhia and Juan Lavista},

      year={2024},

      eprint={2405.12930},

      archivePrefix={arXiv},

}

```



Also, don't forget to cite our original paper for MegaDetector: 



```

@misc{beery2019efficient,

      title={Efficient Pipeline for Camera Trap Image Review},

      author={Sara Beery and Dan Morris and Siyu Yang},

      year={2019}

      eprint={1907.06772},

      archivePrefix={arXiv},

}

```



## 🤝 Existing Collaborators and Contributors



The extensive collaborative efforts of Megadetector have genuinely inspired us, and we deeply value its significant contributions to the community. As we continue to advance with Pytorch-Wildlife, our commitment to delivering technical support to our existing partners on MegaDetector remains the same.



Here we list a few of the organizations that have used MegaDetector. We're only listing organizations who have given us permission to refer to them here or have posted publicly about their use of MegaDetector.



We are also building a list of contributors and will release in future updates! Thank you for your efforts!



👉 Full list of organizations










>[!IMPORTANT]

>If you would like to be added to this list or have any questions regarding MegaDetector and Pytorch-Wildlife, please [email us](zhongqimiao@microsoft.com) or join us in our Discord channel: [![](https://img.shields.io/badge/any_text-Join_us!-blue?logo=discord&label=PytorchWildife)](https://discord.gg/TeEVxzaYtm)