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/14084