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Activator/
vision_onnx

Name: vision_onnx
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sorush-80m ONNX Model Spec

Exported from checkpoint checkpoints/sorush-80m.ckpt (epoch 4, step 6930).

Property	Value
Architecture	DinoV2 ViT-B/14 + 4 register tokens + projection head
Precision	FP32
File	`onnx_export/sorush-80m.onnx`
Opset	17

Name	Shape	Dtype	Description
`imgs`	`(B, 3, 518, 518)`	float32	RGB images, values in [0, 1]. `B` is dynamic batch size.
`masks`	`(B, 518, 518)`	bool	ROI attention mask. `True` = target region (segment of interest). Pixels outside the target region are strictly isolated in all attention layers to prevent feature contamination.

Name	Shape	Dtype	Description
`embeddings`	`(B, 128)`	float32	Clustering features. Derived from the CLS token → projection head (768 → 1024 → ReLU → 128). Use with cosine similarity or Euclidean distance for clustering.

LoRA weights (r=8, α=16, blocks 8–11) are merged into the backbone. No LoRA ops in the graph.
CBP features are not used (training config: use_cbp_feature=False).
Input images must be exactly 518×518 — the positional embedding interpolation path is not active at this resolution.
masks can be all-True (no isolation) or partial (bidirectional isolation between target/background regions).