TECHNOLOGY COMPARISON / TEN MATCHED EXAMPLES
Compare SAM3, DINOv3, and our forced focused attention on the same image window. Two examples from each of five datasets reveal how masks, scene features, and local features differ.
MATCHED VIEWS / ORIGINAL + THREE TECHNOLOGIES
The original photograph gets the same space as every result. Click a panel for enlarged inspection of the matched views.
READING THE COMPARISON
Ask which view preserves the relevant fine structure. A visually detailed map alone does not establish segmentation accuracy or species recognition.
Actual image-only Hugging Face mask generation using Sam3TrackerModel. No text, manual points, boxes, labels, or object requests were supplied. The pipeline internally generates a default 32 × 32 point grid.
FP32; batch of four points; predicted IoU threshold 0.88; stability threshold 0.95; no crop layers. Highest predicted IoU resolves overlapping accepted masks. The panel overlays these masks on RGB; unassigned background remains visible.
A single pass supplies features with scene-wide context. The panel displays three PCA–varimax axes from the same 16-dimensional retained representation. It is a feature visualization rather than an automatic instance-mask output.
The entire working image window is supplied to the encoder in one pass. This baseline does not use local clipping or four-flip averaging.
The same DINOv3 encoder examines enlarged local 5% or 10% crops. Four inverse-aligned flips and approximately 75% overlap blend local observations into a feature field.
Inspect PCA–varimax, automatic K-means, or regularized GMM. Feature groups may separate textures within one organism or combine several organisms; they are not labelled biological instances.