| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - image-classification |
| - vision |
| - camouflage |
| - military |
| - osint |
| - dinov2 |
| pipeline_tag: image-classification |
| library_name: transformers |
| base_model: facebook/dinov2-small |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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`: |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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). |
|
|
