| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| --- |
| |
| # GaNet (MixVarGENet) |
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| GaNet models lanes as "root keypoints + along-line offsets": the network predicts an 8× downsampled heatmap to locate lane starting points and regresses per-point offsets to reconstruct the full lane; the decoder filters keypoints by `kpt_thr` threshold, clusters and merges points on the same lane via `cluster_thr`, and finally maps back to original image coordinates. |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | GaNet | Single frame `1x3x320x800` | MixVarGENet | `GaNetNeck` | Lane point sequences `(B,L,P,2)` | |
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| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | F1Score (F1) | 0.7937 | 0.791 | — | 0.7908 | |
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| > Results are based on `march = March.NASH_M` (J6M) configuration; this task has no QAT stage (qat column is —). |
| > |
| > HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10. |
| |
| ### Performance Metrics |
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| > **Performance measurement**: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage. |
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| | March | latency (ms) | fps | Memory Usage | |
| |---|---|---|---| |
| | J6M | 0.68 | 2258.92 | 6.60 | |
| | J6P | 0.59 | 8623.54 | 6.80 | |
| | J6B | - | - | - | |
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| J6B performance is not available for this model. |
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| --- |
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| ## Model Overview |
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| ### Core Design |
| |
| GaNet models lanes as "root keypoints + along-line offsets": the network predicts an 8× downsampled heatmap to locate lane starting points and regresses per-point offsets to reconstruct the full lane; the decoder filters keypoints by `kpt_thr` threshold, clusters and merges points on the same lane via `cluster_thr`, and finally maps back to original image coordinates. |
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| - **Task type**: Lane detection (Lane Detection). |
| - **backbone**: MixVarGENet (`include_top=False`, `output_list=[2, 3, 4]` three-scale features, corresponding to stride 8/16/32). |
| - **neck**: `GaNetNeck`: FPN (three-scale fusion to `hid_dim=32`) + Attention module (`attn_ratio=4`, position encoding `pos_shape=(1,10,25)`). |
| - **Detection head**: `GaNetHead` (outputs `hid_dim=32` dimensional features). |
| - **Target generation**: `GaNetTarget` (`hm_down_scale=8`, keypoint radius `radius=2`). |
| - **Post-processing**: `GaNetDecoder` (`root_thr=1`, `kpt_thr=0.4`, `cluster_thr=5`, `downscale=8`). |
| - **Loss**: `GaNetLoss`: `LaneFastFocalLoss` (keypoint classification, weight=1.0) + `L1Loss` (per-point offset regression weight=0.5 + integer offset regression weight=1.0). |
| - **Model input**: Single frame, size `320 × 800` (`FixedCrop` takes original region `(0,270,1640,320)` then `Resize` to 320×800; original resolution 1640×590). |
| - **Model output**: Ordered point sequences per lane (original image coordinate system), obtained via `GaNetDecoder` from heatmap keypoint clustering + offset decoding. |
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| **Deployment note**: The deployment graph (`deploy_model`) removes `targets`/`post_process`/`losses`, keeping only backbone + neck + head; output name is `pred` heatmap features; `GaNetDecoder` is re-attached during inference (float/hbir/hbm infer) to complete lane decoding. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/Wolfwjs/GANet |
| Paper: https://arxiv.org/abs/2204.07335 |
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| Note: backbone MixVarGENet is HEAL-developed. |
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14098 |
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