CamoNet

A fine-grained military camouflage pattern classifier.

Given a photo of a uniform, vehicle, or fabric fragment, CamoNet predicts which of 40 historical and contemporary military camouflage patterns it belongs to — along with country of origin, era, and visual family.

Built on a fine-tuned DINOv2 backbone (facebook/dinov2-small), chosen because its self-supervised ImageNet pre-training learns the kind of dense texture features that camouflage classification depends on.

Quick start

from transformers import pipeline

clf = pipeline("image-classification", model="Mattysmittttt/camonet")
clf("path/to/uniform.jpg", top_k=3)
# [{'label': 'us_marpat_woodland', 'score': 0.91},
#  {'label': 'ca_cadpat_tw',       'score': 0.04},
#  {'label': 'us_aor2',             'score': 0.02}]

For richer output (origin, era, family) use the bundled taxonomy.py:

from huggingface_hub import hf_hub_download
import importlib.util, sys

path = hf_hub_download("Mattysmittttt/camonet", "taxonomy.py")
spec = importlib.util.spec_from_file_location("camonet_tax", path)
tax = importlib.util.module_from_spec(spec); spec.loader.exec_module(tax)

print(tax.PATTERN_BY_ID["us_marpat_woodland"])
# Pattern(id='us_marpat_woodland', name='MARPAT Woodland',
#         origin='USMC', era='2002-present', family='digital', notes=...)

Pattern coverage (40 classes)

Family	Count	Examples
digital	10	MARPAT, CADPAT, UCP, EMR Digital Flora, Type 07, ROK Granite
blob	8	Flecktarn, AUSCAM, M90, TAZ 90, JGSDF, TTsKO, M75, Splittertarn
desert	7	Chocolate Chip, DCU 3-Color, AOR1, DPM Desert, Tropentarn
multi-terrain	6	MultiCam, OCP Scorpion W2, MTP, AMCU, Partizan, Kryptek Mandrake
woodland	4	M81 Woodland, ERDL, French CCE, Italian Vegetata
brushstroke	4	Tiger Stripe, DPM Woodland, KLMK, VSR-93 Flora
arid	1	A-TACS AU

Full taxonomy with origin and era for each pattern lives in taxonomy.py.

Intended uses

• OSINT & journalism — assist analysts in identifying camouflage patterns in conflict imagery, photographs, and footage.
• Museum & archival cataloguing — auto-tag uniform collections.
• Milsurp & collector tooling — identify unknown patterns from photographs.
• Airsoft, milsim, reenactment — kit verification.
• Educational / reference — interactive teaching tool for military history.

Limitations & out-of-scope use

• Heavily worn, faded, or damaged fabric drifts predictions toward visually adjacent patterns. Treat low-confidence outputs as such.
• Many real-world patterns share design DNA (MARPAT ↔ AOR1/2 ↔ CADPAT). A top-3 read is more honest than top-1 in those cases.
• The model has not been trained on every pattern in existence — it covers ~40 of the most widely-issued. Out-of-distribution patterns will be forced into the closest learned class.
• Not a person/face/identity classifier. It looks at fabric, not faces.
• Not a substitute for expert authentication of historical garments.

Training data

Composed from public web sources:

• eBay militaria listings (clean product photography, well-labeled)
• Reddit r/camo, r/Militariacollecting, r/milsurp (in-the-wild photos)
• Hand-curated reference plates per pattern

Actual scale used for this checkpoint: 3,080 labeled images across 40 patterns (≈77 per pattern average; min 14, max 137), stratified 85/15 train/val (2,635 train / 445 val).

The dataset is not redistributed — only the trained weights are. The scrape pipeline is included in the source repo so the dataset is reproducible.

Training details

Hyperparameter	Value
Backbone	facebook/dinov2-small (22M params)
Image size	224×224
Optimizer	AdamW
Learning rate	5e-5, cosine, 10% warmup
Weight decay	0.05
Batch size	32
Epochs	8 (best checkpoint by top-1)
Augmentation	resize+crop, hflip, ±10° rotation, light blur (no colour jitter — colour matters for camo)
Precision	fp32 (Apple Silicon MPS)

Evaluation

Reported on the held-out 15% validation split (445 images, 40 classes):

Metric	Score
Top-1	0.7371
Top-3	0.8652

Per-class confusion analysis is in eval/confusion.png once you run scripts/evaluate.py.

Bias, risks, ethical considerations

CamoNet identifies fabric patterns, not people, faces, vehicles as units, or military formations. Its outputs are about textile design.

Camouflage classification is well-established public knowledge — pattern catalogues like Camopedia have been freely available for over a decade, and the patterns themselves are visible in countless public photographs, parades, news coverage, and museum exhibits. The model does not provide capabilities beyond what an attentive observer with reference material could already do.

That said, downstream use sits in the OSINT space, where claims based on a single image carry real-world consequences. Users should:

• Treat predictions as one signal among many, not authoritative ID.
• Surface confidence scores, not just top-1 labels.
• Avoid using the model to make claims about individual identity, unit attribution, or operational intent.

Citation

@misc{camonet2026,
  title  = {CamoNet: Fine-Grained Military Camouflage Pattern Classification},
  author = {Smith, Matt},
  year   = {2026},
  url    = {https://huggingface.co/Mattysmittttt/camonet}
}

License

Apache-2.0 for the model weights and code.

Built on facebook/dinov2-base (Apache-2.0).