Pasma v1.0 model card
Card date: 2026-10-21. Status: v1.0 candidate release. Weights are hosted in this repository under Apache-2.0 with retained upstream notices. See the deployment limitations and provenance ledger below.
Model and intended use
Pasma classifies one main garment/accessory in a home photograph for wardrobe entry, catalogue assistance and user-correctable suggestions. Small and Nano use edgenext_small.usi_in1k; Pico uses convnext_pico.d1_in1k. Inputs are 320² or 256² RGB; outputs are 20-way type softmax, six category probability sums, and 21 independent visual-material sigmoids. It needs no background removal model.
Training uses a shared backbone with type, auxiliary coarse and material heads. The exported graph discards the independent coarse prediction and computes prob_coarse = prob_type @ H. There are no weather/style heads in these artifacts. This release follows the later Pasma plan's v3 schema, which differs from the original four-head development specification; the documentation does not imply compliance with that older architecture.
| Variant | Run | Parameters (M) | Best epoch | Local eval T | Export T |
|---|---|---|---|---|---|
| Pasma-Small-320 | r4_edgenext_320 |
5.296 | 27 | 0.8846 | 0.8877 |
| Pasma-Nano-256 | r3_edgenext_s |
5.296 | 30 | 0.8901 | 0.8912 |
| Pasma-Pico-256 | r3_convnext_pico |
8.557 | 39 | 0.9078 | 0.9071 |
Parameter counts are measured for the fitted classification models, not the original ImageNet classifiers (often quoted as 5.6M/9.0M). Export and local-evaluation temperatures come from different recorded fits and must not be silently interchanged. Softmax temperature preserves type ranking but changes calibration and potentially aggregated coarse ranking.
Training and data
The released students' saved configs use real-only training with a fine-tuned vit_so400m_patch16_siglip_256.v2_webli teacher (r2_so400m_teacher). The broader research pipeline includes synthetic data and real/synthetic teacher training; the students are not documented as directly trained on a synthetic/real mixture or an ensemble. See the technical report for the implemented loss.
Common student settings: seed 42, 40 epochs, batch 64, 12,000 sampled examples per epoch, AdamW head LR 5e-4, backbone LR multiplier 0.5, weight decay 0.05, one warm-up epoch followed by cosine decay, EMA 0.999, dropout 0.2, drop-path 0.1, label smoothing 0.1, online type distillation alpha 0.9 at T=2, and auxiliary coarse/material weights 0.3/0.3. The selected checkpoint maximizes validation Top-1 + macro F1. Unknown/low-confidence materials are excluded from material loss.
The fixed v3 manifest is data/splits/v3_splits.csv; ordinary train/validation/test and benchmark-development/test assignments are stored there. No random split is made during evaluation. Embedding/dHash screening reduces known overlap; it is not proof that all physical-item duplicates are absent. The taxonomy/protocol records its scope.
Source-derived type labels and curated expected-material sets have no supplied complete double-annotation/adjudication record. Material targets represent plausible visual labels, not laboratory fibre measurements. Image rights, participant consent, generator/pseudo-label provider provenance and redistribution permissions require a separate release ledger. Private source photos are excluded from the evidence snapshot.
Evaluation
| Model | Input | ONNX FP16 (MiB) | test_real Top-1 [95% CI] | bench_test Top-1 [95% CI] | Combined Top-1 [95% CI] | Combined macro F1 |
|---|---|---|---|---|---|---|
| Pasma-Small-320 | 320² | 10.32 | 90.75% [88.8, 92.6] | 83.53% [78.7, 87.9] | 89.12% [87.4, 90.9] | 87.01% |
| Pasma-Nano-256 | 256² | 10.32 | 90.05% [87.9, 92.0] | 80.72% [75.5, 85.5] | 87.94% [86.0, 89.8] | 85.44% |
| Pasma-Pico-256 | 256² | 16.40 | 89.11% [87.0, 91.2] | 81.93% [77.1, 86.8] | 87.49% [85.5, 89.4] | 84.84% |
Type Top-3 on test_real / bench_test / combined: Small 98.01 / 93.98 / 97.10%; Nano 98.13 / 93.17 / 97.01%; Pico 97.78 / 93.57 / 96.83%. The teacher reaches 93.93% combined Top-1 (95% CI 92.6-95.2); Small reaches 94.9% of that accuracy, a descriptive ratio rather than a statistical equivalence claim.
The type CIs use 2,000 image-level bootstrap resamples, seed 0. They do not model group dependence, label noise, training seed variation or repeated model selection. Local FP32 evaluation uses MPS, no TTA and exif=false; deployment requires EXIF correction. The source Modal BF16 results differ by a few borderline images and are not mixed into these tables.
| Model | Combined coarse accuracy | ECE test_real / bench_test | Material mAP on bench_test [95% CI] | Material supported macro F1 / micro F1 |
|---|---|---|---|---|
| Small-320 | 95.19% | 0.0466 / 0.1016 | 0.664 [0.630, 0.715] | 0.624 / 0.690 |
| Nano-256 | 94.56% | 0.0411 / 0.1059 | 0.654 [0.624, 0.703] | 0.646 / 0.699 |
| Pico-256 | 93.83% | 0.0443 / 0.1059 | 0.638 [0.610, 0.685] | 0.600 / 0.659 |
Material thresholds are fitted on bench_dev, then fixed for bench_test. Only 17/21 classes have positive test support; mAP and supported macro F1 exclude unsupported classes. The exact metrics, 1,000-resample material CIs, all-class F1 and per-class results are in the evidence snapshot.
Per-type recall / F1
Calculated from the saved confusion matrices on real_all_heldout (n=1,103); support is shared across variants. These are point estimates, not per-class confidence intervals.
| Type | Support | Small R / F1 | Nano R / F1 | Pico R / F1 |
|---|---|---|---|---|
| T_SHIRT | 67 | 0.910 / 0.871 | 0.910 / 0.853 | 0.955 / 0.859 |
| SHIRT | 38 | 0.895 / 0.829 | 0.842 / 0.800 | 0.842 / 0.800 |
| BLOUSE | 23 | 0.478 / 0.564 | 0.522 / 0.558 | 0.435 / 0.526 |
| SWEATER | 53 | 0.925 / 0.907 | 0.925 / 0.891 | 0.925 / 0.916 |
| HOODIE | 24 | 0.917 / 0.898 | 0.792 / 0.826 | 0.833 / 0.870 |
| PANTS | 89 | 0.944 / 0.939 | 0.921 / 0.911 | 0.921 / 0.911 |
| JEANS | 21 | 0.857 / 0.923 | 0.810 / 0.872 | 0.714 / 0.769 |
| SHORTS | 28 | 0.893 / 0.893 | 0.786 / 0.830 | 0.857 / 0.889 |
| SKIRT | 59 | 0.949 / 0.949 | 0.949 / 0.926 | 0.932 / 0.948 |
| JACKET | 69 | 0.797 / 0.859 | 0.754 / 0.812 | 0.739 / 0.816 |
| COAT | 117 | 0.966 / 0.930 | 0.940 / 0.905 | 0.949 / 0.906 |
| VEST | 25 | 0.720 / 0.800 | 0.680 / 0.773 | 0.640 / 0.744 |
| DRESS | 67 | 0.910 / 0.897 | 0.896 / 0.916 | 0.851 / 0.884 |
| SHOES | 54 | 0.852 / 0.807 | 0.907 / 0.852 | 0.852 / 0.807 |
| BOOTS | 48 | 0.896 / 0.915 | 0.875 / 0.913 | 0.854 / 0.882 |
| SNEAKERS | 46 | 0.913 / 0.875 | 0.913 / 0.884 | 0.935 / 0.915 |
| SANDALS | 58 | 0.776 / 0.811 | 0.793 / 0.844 | 0.776 / 0.811 |
| ACCESSORY | 154 | 0.942 / 0.942 | 0.955 / 0.942 | 0.968 / 0.931 |
| HAT | 35 | 0.971 / 0.986 | 0.971 / 0.971 | 0.886 / 0.912 |
| SCARF | 28 | 0.750 / 0.808 | 0.750 / 0.808 | 0.857 / 0.873 |
Deployment and numerical behavior
ONNX uses opset 17, batch 1, float32 input/output even for FP16/INT8 internal weights. CoreML uses FP16 MLProgram with an iOS 17 deployment target. FP32 ONNX matches the reference in the saved type-parity test; FP16 and INT8 do not all meet the original max probability error requirement. See deployment for exact measurements and release gates for pending work. M2 CPU+NE p50 is 5.85/4.21/2.27 ms for Small/Nano/Pico; phone latency, peak activation RAM and browser/Android parity are unmeasured.
Limitations and ethical considerations
- BLOUSE is weak: Small recall 11/23 (47.8%) and Top-3 recall 21/23 (91.3%). An exploratory +2.3 logit adjustment is excluded from released graphs/headline metrics; it trades SHIRT recall for BLOUSE recall and needs fresh validation.
- Use Top-3 suggestions and user confirmation. A confidence threshold is a product policy to validate on deployment data; it is not a universal reliability guarantee. Calibration worsens on
bench_test. - Coarse probabilities are consistent marginals, but independent type/category argmax choices can disagree. Map the chosen type to its category for a consistent displayed pair.
- Multiple garments, tiny/cropped items, unusual silhouettes, backgrounds and ambiguous taxonomy boundaries can cause failures. There is no explicit unknown/out-of-distribution class or measured rejection benchmark.
- No demographic, geographic, device, lighting or accessibility fairness study is supplied. Performance cannot be assumed uniform across these groups.
- Do not use visual material outputs to certify fibre content, allergy safety, care instructions, price, authenticity or provenance. Appearance can be shared by different fibres and blends.
- Offline inference can keep images on the device; application telemetry/upload behavior is outside this model's control. Cloud benchmark calls send images to external providers and are separate from local inference.
- The private dataset prevents complete independent reproduction from a source checkout. Published aggregate metrics allow inspection, not verification of unavailable labels/photos.
License, maintenance and citation
Project code is Apache-2.0. Intended weight release is Apache-2.0 subject to upstream retained notices and completed provenance checks; see license ledger. No benchmark-only third-party weights or segmentation weights are included. This card does not grant rights to source datasets.
Report issues through the repository issue tracker with runtime, model SHA-256 and a non-sensitive reproduction. Cite this card as Pasma v1.0: Compact Clothing Classification for Home Wardrobe Photographs, technical report, 2026; a canonical URL/DOI and named author list will be added at publication. No paper acceptance or arXiv submission is claimed.