garment-attributes

Multi-label classification of fine-grained garment construction attributes from a garment crop: silhouette, length, neckline/collar/sleeve/pocket type, opening/closure type, waistline, textile pattern, finishing techniques, and fabric appearance. SigLIP2 vision encoder (Apache 2.0) with a classification head, fine-tuned on per-instance attribute annotations from Fashionpedia (CC BY 4.0).

Label space: the 218 Fashionpedia attributes with ≥100 training instances (the full list with supergroups and support counts ships in label_space.json and in config.json id2label). Attributes map to tech pack fields — e.g. opening type → construction/closure, textile pattern → BOM/fabric — per the pipeline's docs/taxonomy.md.

Loads with stock transformers — no remote code:

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

model = AutoModelForImageClassification.from_pretrained("resoa/garment-attributes")
proc = AutoImageProcessor.from_pretrained("resoa/garment-attributes")
img = Image.open("garment_crop.jpg")  # crop of ONE garment, not a full scene
probs = torch.sigmoid(model(**proc(images=img, return_tensors="pt")).logits)[0]
for i, p in enumerate(probs):
    if p > 0.5:
        print(model.config.id2label[i], round(p.item(), 3))

Intended use

• Estimate visible construction characteristics of a manufactured garment from a photo (crop the garment first — use garment-detector-seg).
• Cross-check a sample against tech pack attributes ("single-breasted", "welt pockets", "no distressing").

Important: input must be a single-garment crop. Full-scene inputs degrade accuracy sharply; that is what the detector stage is for.

Out of scope: fiber content, GSM, measurements, stitch class, color (color is measured deterministically in the pipeline, not classified).

Training

• Base: google/siglip2-base-patch16-224 (Apache 2.0).
• Data: 156,937 Fashionpedia per-instance crops with attribute labels (scripts/prepare_attribute_data.py; 8% bbox padding, min crop 48 px, attributes with 100+ train instances).
• Objective: sigmoid BCE (problem_type="multi_label_classification").
• Recipe: scripts/train_attributes.py with configs/attributes_mac.yaml (4 epochs, batch 32, lr 2e-5, cosine schedule, trained on Apple-silicon MPS in ~4.5 hours; val metrics improved monotonically each epoch).

Evaluation (Fashionpedia val2020 instances)

Metric	Value
macro mAP	0.442
micro F1 @ 0.5	0.710
macro F1 @ 0.5	0.306
eval instances	3,711
evaluable labels	212 of 218

Read the per-attribute table (per_attribute.md, shipped in this repo) before using any single attribute for QC decisions: performance varies widely by attribute, and rare attributes (support near the cutoff) can be substantially weaker. We publish the full table, including the bad rows, on purpose.

Known limitations & biases

• Multi-label confidences are not calibrated probabilities; the reference pipeline routes 0.5–0.7 confidence predictions to human review.
• Absence of a prediction is weak evidence of absence (occlusion, angle).
• Trained on worn-garment street photos; flat-lay/factory photos are out-of-domain — fine-tune on in-domain crops for production use.
• Attribute definitions follow Fashionpedia's expert ontology, which may differ from a specific factory's terminology; the supergroup mapping in label_space.json is the bridge.

License & attribution

Weights: Apache 2.0. Data: Fashionpedia, CC BY 4.0 — cite Jia et al., ECCV 2020. Base model: SigLIP2 (Google, Apache 2.0), Tschannen et al., 2025.