ImageClassifierYOLO

Uses an Ultralytics classification model to predict a class for an image or crop, returning a Link to that input.

Install its optional dependencies with pip install "collectra[yolo]".

classify:
  type: collectra.ImageClassifierYOLO
  model: models/specimens-cls.pt
  input: image
  output: [Pollen, Spore]

Commands

run INPUTS...: classify inputs with the configured model and the shared run options.
train INPUTS...: train from labelled result directories. Options include --model, --imgsz, --epochs, --batch, regularization, and augmentation.

collectra --pipeline pipeline.yaml task classify run images --output results
collectra --pipeline pipeline.yaml task classify train training-data --validation validation --epochs 20
collectra --pipeline pipeline.yaml task classify run --help
collectra --pipeline pipeline.yaml task classify train --help

Use a classification checkpoint with the appropriate output labels.

Multichannel TIFFs

ImageClassifierYOLO supports multichannel TIFFs during training and inference, including cropped images. No channel option is needed: Collectra inspects every prepared training and validation image and rejects inconsistent channel counts with filenames and detected counts. The dataset determines the model’s input channels; Ultralytics constructs the model and transfers compatible weights.

TIFF axes metadata determines channel order, supporting channel-first and channel-last layouts (CYX, YXC, SYX, YXS), grayscale images, and unlabelled page stacks. Ambiguous multidimensional TIFFs are rejected. TIFF crops retain all bands and their original dtype. Whole TIFFs are copied intact.

TIFFs and non-RGB datasets use a custom Ultralytics classification trainer and tensor loader. Unsigned integer samples are divided by their dtype’s maximum (255 for uint8, 65535 for uint16); floating-point values are retained as float32. Normalization is identity: RGB statistics are not applied to spectral bands. Training uses random resized crops, --fliplr, --flipud, and --erasing. RGB colour jitter and --auto-augment are skipped on this path, with a log message explaining the policy. Validation and inference use resize and centre crop. RGB sample previews are skipped, while metrics and checkpoints remain available. Ordinary three-channel non-TIFF datasets retain the standard Ultralytics classification path.

Use ImageClassifierYOLO to run saved multichannel checkpoints: it applies the saved tensor transforms and bypasses Ultralytics’ RGB prediction loader. Calling a checkpoint directly through the stock Ultralytics classification CLI does not enable this custom data path. Inference requires an input with the same channel count as the model.

The custom trainer is tested against Ultralytics 8.4.78 on CPU. GPU and distributed execution have not been exercised by the unit tests. No additional dependencies are required beyond Collectra’s existing TIFF and PyTorch packages.