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name: detection description: "Operate Pytorch-Wildlife image detectors for camera-trap and overhead wildlife localization, including MegaDetector V5/V6, HerdNet, OWL, public variants, local weights, batching, and result interpretation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Detection

Use this skill when the request is to locate animals, people, or vehicles in images with Pytorch-Wildlife 1.3.0. It covers camera-trap images, overhead wildlife imagery, single-image inference, image-folder batches, local detector weights, device choice, confidence thresholds, and the standard supervision.Detections result contract.

Load the linked references for exact signatures, model/version tables, output caveats, and recovery steps:

• model overview
• API reference
• workflows
• troubleshooting
• environment checker

Route the request

1. Decide whether the imagery is ordinary camera-trap imagery or overhead/aerial imagery. Start with MegaDetector V6 for ordinary wildlife detection. Use OWL-C or OWL-T for overhead imagery and HerdNet for dense-herd localization or counting-oriented workflows.
2. Select the wrapper and exact version before constructing it. V5 uses a or b; the standard V6 wrapper accepts only the five MDV6-* values documented in the API reference. MIT and Apache V6 variants are separate classes.
3. Decide whether weights may be fetched. For offline or custom-weight work, pass an existing local checkpoint with weights=... and use device="cpu" unless the checkpoint and CUDA stack have been verified.
4. Pick single_image_detection for one path or RGB HWC ndarray. Pick batch_image_detection for an image directory; only standard Ultralytics V6/Deepfaune wrappers also accept a sequence of RGB HWC ndarrays.
5. Inspect result["detections"] before post-processing. Route JSON, image annotation, crop/separation, and video output to data-and-postprocessing. Route detector-crop classification to classification, and training or fine-tuning to fine-tuning.

Minimal public usage

from PytorchWildlife.models import detection as pw_detection

model = pw_detection.MegaDetectorV6(
    device="cpu", weights="/path/to/local/model.pt",
    version="MDV6-yolov10-c",
)
one = model.single_image_detection("capture.jpg", det_conf_thres=0.20)
many = model.batch_image_detection("captures/", batch_size=16,
                                   det_conf_thres=0.20)

The path above is an input placeholder, not a required package location. Do not construct a model with default pretrained settings in a no-network run. The constructors can load weights during construction; the safe environment checker never constructs a model.

Quick selection checklist

• General camera-trap screening: standard MegaDetectorV6.
• Existing V5 workflow or checkpoint: MegaDetectorV5.
• Dense herds: HerdNet; overhead imagery: OWLC or OWLT.
• Licensing/backend-specific V6 needs: use the separate MIT or Apache wrapper.

Result contract

For the normal detector wrappers, a result is a dictionary containing an image identifier (img_id, or an image object for some ndarray calls), a supervision.Detections object, and formatted labels. The detections object contains xyxy (N x 4 pixel boxes), confidence (N), and class_id (N). MegaDetector classes map 0=animal, 1=person, and 2=vehicle; always inspect model.CLASS_NAMES for HerdNet, OWL, or third-party wrappers because their class maps differ. normalized_coords, when supplied, is intended to contain [x1/W, y1/H, x2/W, y2/H]; see the API reference before trusting it across all wrappers.

Operational guardrails

• Confidence thresholds default to 0.2 and are applied at inference. Validate caller-provided values in [0, 1]; the package does not provide a universal range check.
• HerdNet applies both det_conf_thres and clf_conf_thres, using strict greater-than comparisons. OWL has only a detection threshold.
• HerdNet, OWL-C, and OWL-T use patch/stitch inference and their source batch loops effectively process the first image of each loaded batch. Use batch_size=1 for predictable operation.
• RGB arrays should be HWC (height, width, 3), not CHW. A directory batch recursively discovers common image extensions and preserves source paths.
• id_strip is passed to Python string .strip, so it removes matching characters at the ends rather than a path prefix.
• Default pretrained constructors may access the network and cache files. Missing cache files, optional legacy imports, and CUDA availability are operational prerequisites, not detection results.

For exact constructor signatures, supported versions, coordinate caveats, custom-weight behavior, and specialized wrappers, use the references rather than inferring from this router.