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name: classification description: "Route and execute PytorchWildlife wildlife image-classification workflows, including pretrained, custom-weight, batch, and detector-crop inference." disable-model-invocation: true metadata: disco-role: operating license: MIT

Classification

Use this sub-skill when the request is to classify wildlife images or crops, choose a PytorchWildlife classifier, load a local classifier checkpoint, run single/batch inference, or attach species labels to detector animals. The package target is PytorchWildlife 1.3.0 on Python >=3.10.

Route first

• Choose a classifier from the model overview before constructing it. Check the model's class count, label vocabulary, language (Deepfaune), input size, and checkpoint format.
• Use single_image_classification for one RGB image and batch_image_classification for an image directory or detector results. The exact signatures and result-shape caveats are in the API reference.
• For a detector-to-classifier request, let the detection workflow construct and run the detector, then pass its results to the crop workflow described in pipelines. Do not configure detectors here; route that part to ../detection/SKILL.md.
• Route JSON/timelapse/image/video serialization to ../data-and-postprocessing/SKILL.md; route training or fine-tuning to ../fine-tuning/SKILL.md.

Model selection

• AI4GAmazonRainforest: PlainResNet, 36 classes, version="v1" or version="v2"; v2 is the constructor default.
• AI4GOpossum: binary Opossum/non-opossum sigmoid classifier.
• AI4GSnapshotSerengeti: 10-class PlainResNet classifier (nine named animals plus other).
• DeepfauneClassifier: TIMM/DINOv2 classifier; select class_name_lang explicitly when labels must be French, English, Italian, or German.
• DFNE: the Deepfaune-New-England TIMM classifier with its fixed English vocabulary.
• CustomWeights: PlainResNet-50 checkpoint loader. Supply complete, zero-based class_names matching the checkpoint output dimension; inspect a local checkpoint with the bundled diagnostic helper before constructing it.

pretrained=True may download model weights. Construction and inference do not imply that weights are cached. Prefer an explicit local weights path in offline or reproducible runs; never start a download merely to diagnose an API or shape problem.

Safe operating sequence

1. Normalize the input: use a readable RGB path or an HWC RGB array for single inference; use a directory of supported image extensions for folder inference. Use Classification_Inference_Transform unless a model-specific transform is required.
2. Instantiate the selected class with an explicit device ("cpu" or a verified CUDA device), local weights when available, and the exact model parameters in the reference tables. Do not use pretrained constructors in a no-network check.
3. Call the single or batch method with exactly one source: data_path or det_results. Preserve the returned order; the DataLoader does not shuffle, but filesystem traversal order should not be mistaken for sorted order. TIMM models additionally expose batch_size and num_workers.
4. Validate each result's img_id, prediction, class_id, and confidence. Treat all_confidences as a full class distribution only for models that emit it and only with the batch caveat documented in the API reference.
5. If crops came from detections, retain a parallel (img_id, detection index, bbox) mapping while building or consuming crops. Do not align labels by a single global counter across all detections: DetectionCrops filters to animal class 0 and returns only those crops.
6. If anything fails, use troubleshooting and run the read-only checkpoint helper before changing model architecture or class names.

Input and output contract

ClassificationImageFolder yields (tensor, image_path) and recursively accepts common raster extensions. DetectionCrops yields (tensor, crop_path) and retains only detections whose class_id == animal_cls_id (default 0). The classifier result is a dictionary for single inference and a list of such dictionaries for batch inference. Most multiclass implementations emit:

{"img_id", "prediction", "class_id", "confidence", "all_confidences"}

AI4GOpossum emits the first four fields only. See the API reference before feeding results to a serializer, and route serialization to data-and- postprocessing rather than inventing a new output schema here.

Boundaries and limitations

• This skill does not select thresholds or construct detector models.
• It does not download weights, launch a service, run training, or claim a CUDA forward pass unless that pass was separately verified.
• The package eagerly imports many model families. A legacy yolov5 compatibility failure can prevent importing classification even when the classifier code itself is usable; apply the actionable recovery in troubleshooting without publishing environment-specific shims.
• The source's id_strip is Python string strip behavior, not a guaranteed path-prefix removal. Normalize identifiers explicitly before relying on exact joins.

Bundled references

• Model overview
• API reference
• Detector-crop and batch pipelines
• Troubleshooting
• Checkpoint/result diagnostic