CLI

These flags allow you to define the kind of task and the model to process your image or a directory with images. Check out how to use them in Command Line.

-i path/to/dir/or/file, --input path/to/dir/or/file

Path to the input image or the directory with images.

-o path/to/dir/or/file, --output path/to/dir/or/file

Path to the output file or the directory. If not provided, then, if input is a file, the prediction will be printed (or shown if it is an image), otherwise, if input is a directory, the predictions will be written to a directory with the same name with an added suffix _preds. If provided as a file, then the prediction(-s) will be saved to this file (supported extensions include: .txt, .csv, .json, .npy, .pkl, .jpg, .png). If provided as a directory, then the predictions will be saved to this directory use --extension flag to specify the file extensions in that directory.

Default: None

-e <ext>, --extension <ext>

Only used if --output is a directory. The extension to use to save the predictions as files. Common extensions include: .txt, .csv, .json, .npy, .pkl, .jpg, .png. If not specified, it will be set automatically to .jpg for image predictions and to .txt for all other formats.

Default: None

-f <format>, --format <format>

The format to use to map the raw prediction to.

• For classification, common formats are bool, proba, str - check GlassesClassifier.predict for more details

• For detection, common formats are bool, int, img - check GlassesDetector.predict for more details

• For segmentation, common formats are proba, img, mask - check GlassesSegmenter.predict for more details

If not specified, it will be set automatically to str, img, mask for classification, detection, segmentation respectively.

Default: None

-t <task-name>, --task <task-name>

The kind of task the model should perform. One of

• classification

• classification:anyglasses

• classification:sunglasses

• classification:eyeglasses

• classification:shadows

• detection

• detection:eyes

• detection:solo

• detection:worn

• segmentation

• segmentation:frames

• segmentation:full

• segmentation:legs

• segmentation:lenses

• segmentation:shadows

• segmentation:smart

If specified only as classification, detection, or segmentation, the subcategories anyglasses, worn, and smart will be chosen, respectively.

Default: classification:anyglasses

-s <model-size>, --size <model-size>

The model size which determines architecture type. One of small, medium, large (or s, m, l).

Default: medium

-b <batch-size>, --batch-size <batch-size>

Only used if --input is a directory. The batch size to use when processing the images. This groups the files in the input directory to batches of size batch_size before processing them. In some cases, larger batch sizes can speed up the processing at the cost of more memory usage.

Default: 1

-p <pbar-desc>, --pbar <pbar-desc>

Only used if --input is a directory. It is the description that is used for the progress bar. If specified as "" (empty string), no progress bar is shown.

Default: "Processing"

-w path/to/weights.pth, --weights path/to/weights.pth

Path to custom weights to load into the model. If not specified, weights will be loaded from the default location (and automatically downloaded there if needed).

Default: None

-d <device>, --device <device>

The device on which to perform inference. If not specified, it will be automatically checked if CUDA or MPS is supported.

Default: None