Initial CamoNet release

README.md

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+---
+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).

checkpoint-581/config.json

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+    "us_ocp_scorpion": 8,
+    "us_tigerstripe": 11,
+    "us_ucp": 6
+  },
+  "layer_norm_eps": 1e-06,
+  "layerscale_value": 1.0,
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_attention_heads": 6,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "out_features": [
+    "stage12"
+  ],
+  "out_indices": [
+    12
+  ],
+  "patch_size": 14,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "reshape_hidden_states": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7",
+    "stage8",
+    "stage9",
+    "stage10",
+    "stage11",
+    "stage12"
+  ],
+  "transformers_version": "5.7.0",
+  "use_cache": false,
+  "use_mask_token": true,
+  "use_swiglu_ffn": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f031d15334bc1698fb596012bd81714f63fd681fd59d5449676adf48a646df9c
+size 88374736

preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "BitImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  }
+}

taxonomy.py

@@ -0,0 +1,143 @@
+"""
+CamoNet pattern taxonomy.
+
+Each pattern has:
+  - id:       short slug used as the class label (stable, snake_case)
+  - name:     human-readable display name
+  - origin:   country / military of origin
+  - era:      rough date range of issue
+  - family:   visual family (woodland / desert / arid / digital / brushstroke / blob / multi-terrain)
+  - notes:    short blurb for the model card
+
+Keep this list curated, not exhaustive. ~40 patterns is the sweet spot:
+big enough to be impressive, small enough to actually train with limited data.
+"""
+
+from dataclasses import dataclass
+from typing import Literal
+
+Family = Literal[
+    "woodland", "desert", "arid", "digital", "brushstroke",
+    "blob", "multi-terrain", "winter", "urban", "naval"
+]
+
+
+@dataclass(frozen=True)
+class Pattern:
+    id: str
+    name: str
+    origin: str
+    era: str
+    family: Family
+    notes: str
+
+
+PATTERNS: list[Pattern] = [
+    # --- United States ---
+    Pattern("us_erdl", "ERDL", "United States", "1948-1980s", "woodland",
+            "Early US 4-color woodland pattern, used in Vietnam."),
+    Pattern("us_m81_woodland", "M81 Woodland", "United States", "1981-2006", "woodland",
+            "Iconic 4-color US woodland; the BDU pattern."),
+    Pattern("us_dcu_chocolate_chip", "Chocolate Chip (DBDU)", "United States", "1981-1991", "desert",
+            "6-color desert with pebble-like spots; Gulf War era."),
+    Pattern("us_dcu_3color", "3-Color Desert (DCU)", "United States", "1990-2000s", "desert",
+            "Coffee-stain pattern that replaced Chocolate Chip."),
+    Pattern("us_marpat_woodland", "MARPAT Woodland", "USMC", "2002-present", "digital",
+            "USMC digital woodland; first widely-issued pixelated camo."),
+    Pattern("us_marpat_desert", "MARPAT Desert", "USMC", "2002-present", "digital",
+            "USMC digital desert variant of MARPAT."),
+    Pattern("us_ucp", "UCP (ACU)", "US Army", "2004-2019", "digital",
+            "Universal Camo Pattern; grey-green digital, controversially ineffective."),
+    Pattern("us_multicam", "MultiCam", "United States (Crye)", "2010-present", "multi-terrain",
+            "Crye Precision blended multi-environment pattern; OEF-CP, OCP."),
+    Pattern("us_ocp_scorpion", "OCP Scorpion W2", "US Army", "2015-present", "multi-terrain",
+            "Army's MultiCam-derivative replacement for UCP."),
+    Pattern("us_aor1", "AOR1", "US Navy/NSW", "2010-present", "desert",
+            "NSW desert digital, MARPAT-derived."),
+    Pattern("us_aor2", "AOR2", "US Navy/NSW", "2010-present", "digital",
+            "NSW woodland digital, MARPAT-derived."),
+    Pattern("us_tigerstripe", "Tiger Stripe", "South Vietnam / US SF", "1962-1975", "brushstroke",
+            "Asymmetric horizontal-stripe pattern; many regional variants."),
+
+    # --- United Kingdom ---
+    Pattern("uk_dpm_woodland", "DPM Woodland", "United Kingdom", "1968-2011", "brushstroke",
+            "British Disruptive Pattern Material; brush-stroke 4-color."),
+    Pattern("uk_dpm_desert", "DPM Desert", "United Kingdom", "1990-2011", "desert",
+            "2-color desert DPM."),
+    Pattern("uk_mtp", "MTP (Multi-Terrain Pattern)", "United Kingdom", "2010-present", "multi-terrain",
+            "British MultiCam-derivative with DPM brush-stroke shapes."),
+
+    # --- Germany ---
+    Pattern("de_flecktarn", "Flecktarn", "Germany (Bundeswehr)", "1990-present", "blob",
+            "5-color blob pattern; one of the most effective in temperate forest."),
+    Pattern("de_tropentarn", "Tropentarn", "Germany (Bundeswehr)", "1990s-present", "desert",
+            "3-color arid Flecktarn variant."),
+    Pattern("de_splittertarn", "Splittertarn", "Germany (Wehrmacht)", "1931-1945", "blob",
+            "WW2-era angular splinter pattern."),
+
+    # --- USSR / Russia ---
+    Pattern("ru_klmk", "KLMK", "USSR", "1968-1990s", "brushstroke",
+            "Soviet 'silver leaf' sun-ray 2-color oversuit pattern."),
+    Pattern("ru_ttsko", "TTsKO (Butan)", "USSR", "1984-2000s", "blob",
+            "Three-color Soviet computer-generated pattern."),
+    Pattern("ru_vsr_93", "VSR-93 (Flora)", "Russia", "1993-2000s", "brushstroke",
+            "Vertical brush-stroke 'Flora' pattern."),
+    Pattern("ru_emr_digital_flora", "EMR (Digital Flora)", "Russia", "2008-present", "digital",
+            "Russian Armed Forces digital pattern; pixelated greens."),
+    Pattern("ru_surpat", "SURPAT", "Russia (Survival Corps)", "2010s-present", "digital",
+            "Commercial Russian multi-terrain digital."),
+    Pattern("ru_partizan", "Partizan / Spectre", "Russia (SSO)", "2000s-present", "multi-terrain",
+            "SSO Tactical 'leaf' pattern; layered foliage shapes."),
+
+    # --- Other NATO / Western ---
+    Pattern("ca_cadpat_tw", "CADPAT TW", "Canada", "1997-present", "digital",
+            "Canadian Disruptive Pattern; first issued digital camo (predates MARPAT)."),
+    Pattern("ca_cadpat_ar", "CADPAT AR", "Canada", "2000s-present", "desert",
+            "Arid CADPAT variant."),
+    Pattern("fr_cce", "CCE F1", "France", "1991-2010s", "woodland",
+            "Centre Europe; French M81-style woodland."),
+    Pattern("fr_daguet", "Daguet", "France", "1991-2010s", "desert",
+            "French desert pattern, Gulf War era."),
+    Pattern("it_vegetata", "Vegetata", "Italy", "2004-present", "woodland",
+            "Italian 4-color fractal-style woodland."),
+    Pattern("au_auscam", "AUSCAM (DPCU)", "Australia", "1982-2014", "blob",
+            "Australian 'hearts and bunnies' 5-color blob pattern."),
+    Pattern("au_amcu", "AMCU", "Australia", "2014-present", "multi-terrain",
+            "Australian MultiCam-derivative replacement for DPCU."),
+    Pattern("se_m90", "M90", "Sweden", "1990-present", "blob",
+            "Swedish angular 4-color splinter; very distinctive."),
+    Pattern("ch_taz_90", "TAZ 90", "Switzerland", "1990-present", "blob",
+            "Swiss 5-color leaf/blob pattern."),
+    Pattern("no_m75", "M75", "Norway", "1975-2000s", "blob",
+            "Norwegian 3-color blob pattern."),
+
+    # --- Asia ---
+    Pattern("cn_type07_universal", "Type 07 Universal", "China (PLA)", "2007-present", "digital",
+            "Chinese Type 07 woodland-leaning digital."),
+    Pattern("cn_type07_desert", "Type 07 Desert", "China (PLA)", "2007-present", "digital",
+            "Type 07 arid variant."),
+    Pattern("kr_granite", "ROK Granite", "South Korea", "2014-present", "digital",
+            "Korean digital pattern with granite-like color blocks."),
+    Pattern("jp_jgsdf", "JGSDF Type II", "Japan", "1991-present", "blob",
+            "Japan Ground SDF pinkish-brown tinted blob pattern."),
+
+    # --- Commercial / specialty ---
+    Pattern("commercial_kryptek_mandrake", "Kryptek Mandrake", "United States (commercial)", "2012-present", "multi-terrain",
+            "Commercial layered reptilian-scale pattern."),
+    Pattern("commercial_atacs_au", "A-TACS AU", "United States (commercial)", "2009-present", "arid",
+            "Commercial arid-urban 'pixelated organic' pattern."),
+]
+
+
+PATTERN_BY_ID: dict[str, Pattern] = {p.id: p for p in PATTERNS}
+LABELS: list[str] = [p.id for p in PATTERNS]
+LABEL2ID: dict[str, int] = {label: i for i, label in enumerate(LABELS)}
+ID2LABEL: dict[int, str] = {i: label for i, label in enumerate(LABELS)}
+NUM_LABELS: int = len(LABELS)
+
+
+if __name__ == "__main__":
+    print(f"CamoNet taxonomy: {NUM_LABELS} patterns across {len(set(p.family for p in PATTERNS))} families")
+    for fam in sorted(set(p.family for p in PATTERNS)):
+        members = [p for p in PATTERNS if p.family == fam]
+        print(f"  {fam:15s} ({len(members):2d}): {', '.join(m.id for m in members)}")

training_args.bin

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+size 5265