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name: pytorch-wildlife description: "Use Pytorch-Wildlife for conservation AI workflows involving camera-trap and overhead image detection, wildlife classification, bioacoustic audio pipelines, result post-processing, video demos, and legacy fine-tuning data preparation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Pytorch-Wildlife

Use this skill when a task names Pytorch-Wildlife, PytorchWildlife, MegaDetector, HerdNet, OWL, Deepfaune, passive-acoustic wildlife monitoring, or the companion camera-trap fine-tuning modules. It is a router for the versioned public package and its documented companion workflows; it is not a replacement for model weights, datasets, or a project-specific experiment plan.

Start safely

Install the public distribution in an isolated Python environment. The source metadata for this snapshot requires Python >=3.10:

python -m pip install PytorchWildlife

The package has a broad dependency surface, including PyTorch/TorchVision/ TorchAudio, supervision==0.23.0, gradio>=6.15.1,<7, Ultralytics, YOLOv5, timm, Lightning, and audio packages for bioacoustics. For a CUDA install, select a PyTorch wheel compatible with the host driver before installing the package. Do not use a default pretrained constructor merely to test imports: model constructors may download weights.

Verify the installation without model construction:

python -c "import PytorchWildlife; print(PytorchWildlife.__version__)"

If the import fails in a modern environment around legacy yolov5 and pkg_resources, read the cross-cutting troubleshooting reference before changing package versions.

Route by user intent

• Image detection and localization — read detection for MegaDetector V5/V6, HerdNet, OWL, devices, thresholds, batches, local weights, and detection results.
• Species or image classification — read classification for pretrained/custom classifiers, Deepfaune, batch inputs, and detector-crop classification.
• Audio and passive acoustics — read bioacoustics for YAML configs, annotated windows, mel spectrograms, checkpoints, training/inference flags, and CSVs.
• Data, output, video, or UI — read data-and-postprocessing for image-folder datasets, transforms, JSON/visual outputs, safe separation, video callbacks, and Gradio/Docker caveats.
• User dataset adaptation or fine-tuning — read fine-tuning for the legacy classification and detection companions, split design, YAML/CSV/YOLO preflight, and weight handoff. Do not start training by default.

For a multi-stage request, route each stage to its owner rather than copying all model and output details into one plan. A common image pipeline is: detection → classification (only animal crops) → data-and-postprocessing. An audio pipeline is: bioacoustics preparation → training or checkpoint inference → CSV review.

Cross-cutting operating rules

1. Decide whether the task permits network access, model-weight downloads, GPU execution, external credentials, and writes before running a command.
2. Prefer explicit local weights= and explicit device= values for reproducible or offline work. CPU is a valid structural fallback, but a CPU check does not prove CUDA throughput or model behavior.
3. Validate image/audio paths, label/class mappings, config ranges, and output containment before expensive inference or training.
4. Preserve source files. Post-processing helpers write copies/outputs; keep generated outputs outside recursively scanned input directories.
5. Treat the companion fine-tuning directories as legacy/experimental and keep their environment and checkpoints separate from core package inference.
6. Do not launch Gradio, pull Docker images, download external datasets, or run long training as an implicit “smoke test.”

Public package map

• PytorchWildlife.models.detection: image/localization wrappers and model families.
• PytorchWildlife.models.classification: pretrained and custom image classifiers.
• PytorchWildlife.models.bioacoustics: spectrogram classifiers and checkpoint loading.
• PytorchWildlife.data: image-folder datasets, transforms, and bioacoustic data/config/window/spectrogram helpers.
• PytorchWildlife.utils: video processing and detection/classification output serializers.

Use installation and deployment for prerequisites, CUDA/codec/container choices; API boundaries for verified cross-module contracts; and troubleshooting for import, dependency, weight, path, and backend failures. Read repository provenance before deciding whether this skill matches a checkout or whether a refresh is needed.