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Garments (Fashionpedia)

argus-curator 0.15.2.dev2+g97021d6 @ 97021d6PyPI (opens in new tab)Source (opens in new tab)Changelog (opens in new tab)

Imported from docs/garments.md on argus-curator's dev branch at that commit. Fixes go upstream, not here.

--garments (or "garments": {"enabled": true}) runs a YOLOS-small detector fine-tuned on Fashionpedia (opens in new tab) (valentinafevu/yolos-fashionpedia (opens in new tab), MIT; dataset CC BY 4.0) on every passing image. Each detection records its garment type (27 classes: dress, pants, shoe, bag_wallet, hat, …), probability, box, whether the box touches the frame edge (likely cut off), and a dominant colour from the box's centre. Results land on results[].garments ([] = ran, found nothing) and in the CSV as label:conf:#colour, with * marking a cut-off garment.

Accuracy

Measured on 300 Fashionpedia validation images (CPU, ~0.3 s/image):

Garments, AP50 / mAP0.327 / 0.193
Strong (P / R at 0.5)shoe 0.96 / 0.97 · pants 0.83 / 0.87 · dress 0.75 / 0.91 · hat 0.86 / 0.78 · jacket 0.86 / 0.58
Weakskirt 0.38 / 0.31 · coat 0.43 / 0.15 · shirt/blouse recall 0.31
"Is garment X in the image?", all classesP 0.64 / R 0.76 at 0.3 · 0.77 / 0.66 at 0.5 · 0.86 / 0.56 at 0.7

Fashionpedia's 19 garment parts (sleeve, pocket, zipper, …) scored AP50 0.105 and are not recorded; neither are its 294 fine-grained attributes, which no published checkpoint predicts. Detections are recorded from 0.3 so reviewers see the doubtful ones; filters count them from --min-garment-conf (0.5). A floor below what the scan recorded cannot be honoured, so the server refuses it with 400; the CLI instead records from the lower floor in the first place.

Filters

  • --garment dress,skirt / garments: wearing at least one of these.
  • --exclude-garment bag_wallet,umbrella / exclude_garments: none of these. Exclusion wants recall, so consider a lower --min-garment-conf for it.
  • --uncropped / garments_uncropped: the matching garment is not cut off.
  • --diversify-outfits / diversify_outfits (with --max-keep): the diversity cap also spreads the selection across outfits (garment types and their colours), so an identity LoRA does not learn one outfit as part of the person.

Exclusions say why: garment-not-found, garment-low-confidence (present, below the floor), garment-excluded, garment-cropped, garments-missing.

Installing the model

The detector is converted to ONNX once:

pip install "argus-curator[garments-convert]"   # torch + transformers, only for this step
argus-curator garments-convert                  # pinned commit + sha256
# -> ~/.cache/argus_curator/models/yolos_fashionpedia.onnx

Point elsewhere with --garments-model / ARGUS_CURATOR_GARMENTS_MODEL.