Initial commit

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

saved/image_recognition_spike/match_spike_dinov2.py

@@ -0,0 +1,82 @@
+"""
+Same spike as match_spike.py, but using DINOv2 instead of CLIP.
+
+DINOv2 is trained with a self-supervised image-only objective specifically
+aimed at instance/fine-grained visual similarity, which is a better fit for
+"is this query photo the same object as this reference photo" than CLIP
+(CLIP is trained for text-image alignment and tends to cluster images by
+generic scene/style rather than object identity).
+
+Usage: .venv\\Scripts\\python.exe match_spike_dinov2.py
+"""
+
+import sys
+from pathlib import Path
+
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModel
+
+HERE = Path(__file__).parent
+REF_DIR = HERE / "images" / (sys.argv[1] if len(sys.argv) > 1 else "reference")
+QUERY_DIR = HERE / "images" / "query"
+
+MODEL_NAME = "facebook/dinov2-base"
+
+
+def load_model():
+    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+    model = AutoModel.from_pretrained(MODEL_NAME)
+    model.eval()
+    return model, processor
+
+
+def embed_image(model, processor, path: Path) -> torch.Tensor:
+    image = Image.open(path).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        features = outputs.last_hidden_state[:, 0, :]  # CLS token
+        features = features / features.norm(dim=-1, keepdim=True)
+    return features.squeeze(0)
+
+
+def main():
+    print(f"Loading {MODEL_NAME}...")
+    model, processor = load_model()
+
+    ref_paths = (
+        sorted(REF_DIR.glob("*.jpg"))
+        + sorted(REF_DIR.glob("*.webp"))
+        + sorted(REF_DIR.glob("*.png"))
+    )
+    query_paths = (
+        sorted(QUERY_DIR.glob("*.jpg"))
+        + sorted(QUERY_DIR.glob("*.webp"))
+        + sorted(QUERY_DIR.glob("*.png"))
+    )
+
+    print(f"Embedding {len(ref_paths)} reference images...")
+    ref_names = [p.stem for p in ref_paths]
+    ref_embeds = torch.stack([embed_image(model, processor, p) for p in ref_paths])
+
+    print(f"Embedding {len(query_paths)} query images...\n")
+    correct = 0
+    for qpath in query_paths:
+        qembed = embed_image(model, processor, qpath)
+        sims = ref_embeds @ qembed
+        ranked = sorted(zip(ref_names, sims.tolist()), key=lambda x: -x[1])
+        top_name, top_sim = ranked[0]
+        expected = qpath.stem.split("_")[0]
+        is_correct = top_name == expected
+        correct += is_correct
+        marker = "OK  " if is_correct else "MISS"
+        print(f"[{marker}] query={qpath.stem:<18} -> best={top_name:<10} sim={top_sim:.4f}")
+        runner_up = ", ".join(f"{n}={s:.3f}" for n, s in ranked[1:4])
+        print(f"         runner-up: {runner_up}")
+
+    print(f"\n{correct}/{len(query_paths)} correct top-1 matches")
+
+
+if __name__ == "__main__":
+    main()