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README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# MOTR (EfficientNet-b3)
+
+MOTR models tracking as per-frame query propagation: detection queries are passed across frames via `QueryInteractionModule`; new targets get new queries and disappeared targets are cleared; training jointly supervises detection and tracking losses.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| MOTR | multi-frame image sequence `1x3x800x1422` | EfficientNet-b3 | — | tracking boxes + track IDs `(B,Q,cls+reg+id)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | MOTA | 0.5837 | 0.5704 | 0.5799 | 0.5767 |
+
+> Data tested with `march = March.NASH_M` (J6M).
+>
+> HEAL versions: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10.
+
+### Performance Metrics
+
+> **Performance test methodology**: FPS is measured with 8 threads on a single core; Latency is measured with single core, single thread; Memory is peak DDR usage.
+
+| March | Metric | latency (ms) | fps | Memory Usage |
+|---|---|---|---|---|
+| J6M | main graph | 8.03 | 128.68 | 65.30 |
+|  | qim | 0.37 | 5120.87 | 6.80 |
+| J6P | main graph | 5.81 | 695.38 | 71.90 |
+|  | qim | 0.36 | 10348.73 | 7.00 |
+| J6B | main graph | - | - | - |
+|  | qim | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+MOTR models tracking as per-frame query propagation: detection queries are passed across frames via `QueryInteractionModule`; new targets get new queries and disappeared targets are cleared; training jointly supervises detection and tracking losses.
+
+- **Task type**: Multi-Object Tracking (MOT).
+- **backbone**: EfficientNet-b3 (`efficientnet`, `model_type=b3`, `include_top=False` removes classification head, `use_se_block=False`, ReLU activation).
+- **neck**: — (no separate neck; backbone features feed directly into `MotrHead`).
+- **detection head**: `MotrHead` + `MotrDeformableTransformer` (`d_model=256`, `num_queries=256`, `dim_feedforward=1024`, Deformable Attention, `in_channels=[384]`).
+- **tracking module**: `QueryInteractionModule` (cross-frame query interaction for end-to-end tracking association).
+- **post-processing**: `MotrPostProcess`.
+- **Model input**: multi-frame image sequence, single-frame size `800 × 1422`, sequence sampling interval 10 frames; main graph `img:1x3x800x1422`, QIM subgraph input is query/feat (no images).
+- **Model output**: per-frame detection boxes + cross-frame track IDs (256 queries, 1 class).
+
+**Deployment note**: MOTR inference is split into a main graph (backbone + head + post_process) and a QIM subgraph (`QueryInteractionModule`, DDR input, no images), exported and compiled separately via `deploy.py` and `deploy_qim.py`; they work together at inference time (see Usage Guide > Export and Compilation).
+
+### Official Repo and Paper
+
+Official repo: https://github.com/megvii-research/MOTR
+Paper: https://arxiv.org/pdf/2105.03247
+
+Note: backbone is EfficientNet-b3; the official repo uses a different backbone.
+
+### Reference
+
+For more J6 chip deployment details, see https://developer.horizon.auto/blog/10353