---
license: apache-2.0
base_model:
- facebook/sam2.1-hiera-tiny
tags:
- sam2
- video
- onnx
- webgpu
---

# SAM 2.1 Hiera-Tiny — full video tracking pipeline (ONNX)

ONNX export of **facebook/sam2.1-hiera-tiny** including the **memory modules**
(memory encoder + memory attention + object pointers) that make SAM2 a real
video tracker — unlike image-only exports, no application-level propagation
heuristics are needed.

Exported from `transformers` (`Sam2VideoModel`, v5.11) and numerically
validated against `propagate_in_video_iterator`: worst per-frame mask IoU vs
the PyTorch reference **0.9967** on a synthetic motion clip, including early
frames where the fixed-shape memory bank is padded.

## Graphs (fp32, float I/O)

| file | inputs | outputs |
|---|---|---|
| `onnx/vision_encoder.onnx` | `pixel_values [1,3,1024,1024]` | `feats0 [1,32,256,256]`, `feats1 [1,64,128,128]`, `feats2 [1,256,64,64]` (raw), `feats2_no_mem` (conditioning-frame variant), `vision_pos_embed [1,256,64,64]` |
| `onnx/mask_decoder.onnx` | `feats0`, `feats1`, `feats2_cond`, `input_points [1,1,N,2]`, `input_labels [1,1,N] int32` | `low_res_mask [1,1,256,256]`, `high_res_mask [1,1,1024,1024]`, `iou [1,1]`, `object_score_logits [1,1,1]`, `object_pointer [1,1,256]` (occlusion-gated, in-graph) |
| `onnx/memory_encoder.onnx` | `feats2`, `high_res_mask`, `object_score_logits [1,1]`, `binarize` (scalar: 1 for point-prompted frames) | `memory_tokens [4096,1,64]`, `memory_pos [4096,1,64]` |
| `onnx/memory_attention.onnx` | `current_vision_features [4096,1,256]`, `current_vision_position_embeddings [4096,1,256]`, `memory [28736,1,64]`, `memory_pos [28736,1,64]` | `conditioned_feats [1,256,64,64]` |
| `onnx/pointer_tpos.onnx` | `normalized_diffs [P]` | `pointer_pos [P,64]` |

`constants.json` carries the memory temporal positional encoding table
(7×64), normalization constants, and shape parameters.

## Tracking loop

1. **Seed frame** (user clicks): `vision_encoder` → decoder on `feats2_no_mem`
   with points → mask, object pointer → `memory_encoder` (`binarize=1`) →
   conditioning memory.
2. **Every later frame**: `vision_encoder` → assemble memory bank
   (conditioning memory uses temporal-PE row 6; up to 6 recent frame memories
   use rows `offset-1`; pad to 7 blocks by duplicating the most recent) +
   16 object pointers split into 4×64 tokens with `pointer_tpos` positional
   encoding (offsets normalized by `min(total_frames,16)-1`; pad by
   duplication) → `memory_attention` → decoder with a single padding point
   (label −1) → mask, pointer → `memory_encoder` (`binarize=0`) → push memory.

Occlusion is handled by the model: when `object_score_logits ≤ 0` the mask is
suppressed in-graph and the object pointer falls back to the learned
no-object pointer; tracking recovers automatically when the object reappears.

Used by [FuzzPuppy's Video Object Tracker](https://www.fuzzpuppy.com/video-object-tracker),
which runs this pipeline fully in-browser on WebGPU.