ganet_mixvargenet

GaNet (MixVarGENet)

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

Model Parameters

Accuracy Metrics

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

Performance measurement: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage.

J6B performance is not available for this model.

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Model Overview

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.

• 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.

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.

Official Repo and Paper

Official repo: https://github.com/Wolfwjs/GANet Paper: https://arxiv.org/abs/2204.07335

Note: backbone MixVarGENet is HEAL-developed.

Reference

For more J6 chip deployment details, see https://developer.horizon.auto/blog/14098