Core Features

🛠️ Core Features

🌐 Unified Framework:

Pytorch-Wildlife integrates four pivotal elements:

▪ Machine Learning Models
▪ Pre-trained Weights
▪ Datasets
▪ Utilities

👷 Our work:

In the provided graph, boxes outlined in red represent elements that will be added and remained fixed, while those in blue will be part of our development.

🚀 Inaugural Model:

We're kickstarting with YOLO as our first available model, complemented by pre-trained weights from MegaDetector. We have MegaDetectorV5, which is the same MegaDetectorV5 model from the previous repository, and many different versions of MegaDetectorV6 for different usecases.

📚 Expandable Repository:

As we move forward, our platform will welcome new models and pre-trained weights for camera traps and bioacoustic analysis. We're excited to host contributions from global researchers through a dedicated submission platform.

🧰 Versatile Utilities:

Our set of utilities spans from visualization tools to task-specific utilities, many inherited from Megadetector.

💻 User Interface Flexibility:

While we provide a foundational user interface, our platform is designed to inspire. We encourage researchers to craft and share their unique interfaces, and we'll list both existing and new UIs from other collaborators for the community's benefit.

Let's shape the future of wildlife research, together! 🙌