Add README.md

README.md

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+---
+license: other
+tags:
+  - heal
+  - horizon
+---
+
+# MixVarGENet
+
+MixVarGENet builds a lightweight backbone with mixed-variant blocks (mixvarge); the first two stages use f2/f4 base blocks, the last two stages use f2_gb16 blocks with groups, downsampling stage by stage to stride 32.
+
+---
+
+## Deployment Metrics
+
+### Model Parameters
+
+| Model | Model Input | Backbone | Neck | Model Output |
+|---|---|---|---|---|
+| MixVarGENet | `1x3x224x224` | MixVarGENet | 5 stage mixvarge blocks | classification logits `(B,1000)` |
+
+### Accuracy Metrics
+
+| March | Metric | float | calibration | qat | hbm |
+| --- | --- | --- | --- | --- | --- |
+| J6M | Accuracy | 0.716 | 0.7116 | — | 0.7116 |
+
+> Data tested with `march = March.NASH_M` (J6M); this task has no QAT stage (`—` in the qat column).
+>
+> 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 | latency (ms) | fps | Memory Usage |
+|---|---|---|---|
+| J6M | 0.36 | 5464.88 | 6.40 |
+| J6P | 0.33 | 9374.11 | 6.40 |
+| J6B | - | - | - |
+
+J6B performance is not available for this model.
+
+---
+
+## Model Overview
+
+### Core Design
+
+MixVarGENet builds a lightweight backbone with mixed-variant blocks (mixvarge); the first two stages use f2/f4 base blocks, the last two stages use f2_gb16 blocks with groups, downsampling stage by stage to stride 32.
+
+- **Task type**: Image Classification.
+- **backbone**: MixVarGENet (`type=MixVarGENet`, 5 stages: stride 2/4/8/16/32, `num_classes=1000`), builds a lightweight backbone with mixed-variant blocks (mixvarge), downsampling stage by stage to stride 32.
+- **neck**: 5 stages of mixvarge blocks: `mixvarge_f2`, `mixvarge_f4`, `mixvarge_f2_gb16` (last two stages use group blocks, gb16), channels 32→32→64→96→160.
+- **classification head**: MixVarGENet built-in fully connected classification head, directly outputs 1000-class logits.
+- **Loss function**: `CEWithLabelSmooth` (cross-entropy with label smoothing).
+- **Model input**: single RGB image at resolution `224 × 224` (`1x3x224x224`).
+- **Model output**: 1000-class prediction logits; argmax gives the predicted class.
+
+### Official Repo and Paper
+
+MixVarGENet is an efficient network architecture proposed by Horizon Robotics, optimized for the Journey series chips.
+
+### Reference
+
+For more J6 chip deployment details, see https://developer.horizon.auto/blog/10387