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name: classification-workflows description: "Use ImageAI ImageNet and custom image classification inference workflows with PyTorch model weights." disable-model-invocation: true metadata: disco-role: operating license: MIT

Classification Workflows

Use this sub-skill when the task is to classify still images with ImageAI's PyTorch classification APIs:

• ImageNet classification with ImageClassification and built-in ImageNet class labels.
• Custom classification inference with CustomImageClassification, an ImageAI-trained .pt/.pth model, and the matching JSON class mapping.
• CPU-forced classification, result_count selection, file/numpy/PIL image inputs, and the returned label/probability arrays.

Do not use this sub-skill for custom model training or dataset preparation; route those requests to custom-training-and-data. Do not use it for object detection or video detection; route those to the detection sub-skills.

Runtime references

• API reference: verified class names, call order, method signatures, model type mapping, input forms, return shapes, and model extension behavior.
• Workflows: ImageNet and custom classification recipes, asset prerequisites, CPU-only commands, validation steps, and in-process examples.
• Troubleshooting: classification-specific recovery for missing dependencies, TensorFlow .h5 files, mismatched model type/weights, bad JSON mappings, image input errors, and warning noise.
• Helper script: parameterized CLI wrapper around ImageClassification and CustomImageClassification that prints JSON and never downloads weights.

For package-wide installation, backend, and model-asset policy, also check the root skill's installation and model assets notes.

Quick routing checklist

1. Ask whether the user has ImageNet release weights or a custom ImageAI-trained classifier. ImageNet mode needs only the model file; custom mode also needs the JSON class mapping produced during training.
2. Match --model-type or the setter method to the architecture used by the weights: mobilenetv2, resnet50, inceptionv3, or densenet121.
3. Reject TensorFlow-era .h5 weights for ImageAI 3.x. Use .pt or .pth; for .h5, explain the ImageAI 2.1.6-or-earlier compatibility path instead of trying to load it.
4. Call the model type setter, setModelPath(...), custom setJsonPath(...) when needed, optional useCPU(), then loadModel() before classifyImage(...).
5. Treat outputs as two parallel arrays: labels: list[str] and probabilities: list[float] in descending score order.

Safe helper use

Use the bundled helper for command-line classification when the user supplies local asset paths:

python scripts/classify_image.py \
  --mode imagenet \
  --model-type resnet50 \
  --model-path /path/to/resnet50-weights.pth \
  --image /path/to/image.jpg \
  --result-count 5 \
  --cpu

For custom classification, add --json-path /path/to/model_classes.json and keep --model-type aligned with the architecture used during training. The helper accepts image files only; in-process API usage can pass str paths, numpy.ndarray, or PIL.Image.Image objects as described in the API reference.