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feat: initial HSAP platform

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Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Chengfang Lu
2026-05-25 16:59:59 +08:00
commit 7c43b44c57
1619 changed files with 373355 additions and 0 deletions
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6
algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_cityscapes_256x512.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_cityscapes_256x512_20201225.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_256x512.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_256x512.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_cityscapes_256x512_fp32.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_cityscapes_256x512_fp32_20201227.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_256x512.py
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_256x512.py

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_cityscapes_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_cityscapes_512x1024_20201219.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_cityscapes_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_gtav_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_gtav_512x1024_20201223.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_gtav_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_gtav_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_pascalvoc_321x321.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_pascalvoc_321x321_20201108.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_pascalvoc_321x321.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_pascalvoc_321x321.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv2_synthia_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv2_synthia_512x1024_20201225.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv2/resnet101_synthia_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv2/resnet101_synthia_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv3_cityscapes_256x512.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv3_city_256x512_20201226.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv3/resnet101_cityscapes_256x512.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv3/resnet101_cityscapes_256x512.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv3_cityscapes_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv3_cityscapes_512x1024_20210322.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv3/resnet101_cityscapes_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv3/resnet101_cityscapes_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/deeplabv3_pascalvoc_321x321.sh Normal file
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#!/bin/bash
# Trained weights: deeplabv3_voc_321x321_20201110.pt
python main_semseg.py --train --config=configs/semantic_segmentation/deeplabv3/resnet101_pascalvoc_321x321.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/deeplabv3/resnet101_pascalvoc_321x321.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/enet_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: enet_baseline_culane_20210312.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/enet_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/enet_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh enet_baseline_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/enet_baseline_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: enet_baseline_tusimple_20210312.pt
python main_landet.py --train --config=configs/lane_detection/baseline/enet_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/enet_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh enet_baseline_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/enet_cityscapes_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: enet_cityscapes_512x1024_20210219.pt
# Step-1: Pre-train encoder
python main_semseg.py --train --config=configs/semantic_segmentation/enet/cityscapes_512x1024_encoder.py --mixed-precision
# Step-2: Train the entire network
python main_semseg.py --train --config=configs/semantic_segmentation/enet/cityscapes_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/enet/cityscapes_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_baseline-aug_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_baseline_tusimple-aug_20210723.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/baseline/erfnet_tusimple_aug.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/erfnet_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh erfnet_baseline_tusimple-aug test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_baseline_culane_20210204.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/erfnet_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/erfnet_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh erfnet_baseline_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_baseline_llamas.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_baseline_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/erfnet_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/baseline/erfnet_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh erfnet_baseline_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/baseline/erfnet_llamas.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_baseline_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_baseline_tusimple_20210424.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/erfnet_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/erfnet_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh erfnet_baseline_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_cityscapes_512x1024.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_cityscapes_512x1024_20200918.pt
python main_semseg.py --train --config=configs/semantic_segmentation/erfnet/cityscapes_512x1024.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/erfnet/cityscapes_512x1024.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_resa_culane.sh Executable file
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#!/bin/bash
# Training
python main_landet.py --train --config=configs/lane_detection/resa/erfnet_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/erfnet_culane.py
# Testing with official scripts
./autotest_culane.sh erfnet_resa_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_resa_tusimple.sh Executable file
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#!/bin/bash
# Training
python main_landet.py --train --config=configs/lane_detection/resa/erfnet_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/erfnet_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh erfnet_resa_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_scnn-aug_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_scnn_tusimple-aug_20210723.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/scnn/erfnet_tusimple_aug.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/erfnet_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh erfnet_scnn_tusimple-aug test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_scnn_culane.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_scnn_culane_20210206.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/erfnet_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/erfnet_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh erfnet_scnn_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_scnn_llamas.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_scnn_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/erfnet_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/scnn/erfnet_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh erfnet_scnn_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/scnn/erfnet_llamas.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/erfnet_scnn_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: erfnet_scnn_tusimple_20210202.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/erfnet_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/erfnet_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh erfnet_scnn_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/fcn_cityscapes_256x512.sh Normal file
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#!/bin/bash
# Trained weights: fcn_cityscapes_256x512_20201226.pt
python main_semseg.py --train --config=configs/semantic_segmentation/fcn/resnet101_cityscapes_256x512.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/fcn/resnet101_cityscapes_256x512.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/fcn_pascalvoc_321x321.sh Normal file
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#!/bin/bash
# Trained weights: fcn_pascalvoc_321x321_20201111.pt
python main_semseg.py --train --config=configs/semantic_segmentation/fcn/resnet101_pascalvoc_321x321.py --mixed-precision
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/fcn/resnet101_pascalvoc_321x321.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/fcn_pascalvoc_321x321_fp32.sh Normal file
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#!/bin/bash
# Trained weights: fcn_pascalvoc_321x321_fp32_20201111.pt
python main_semseg.py --train --config=configs/semantic_segmentation/fcn/resnet101_pascalvoc_321x321.py
# Val
python main_semseg.py --val --config=configs/semantic_segmentation/fcn/resnet101_pascalvoc_321x321.py

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv2_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv2_baseline_culane_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/mobilenetv2_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/mobilenetv2_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh mobilenetv2_baseline_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv2_baseline_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv2_baseline_tusimple_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/mobilenetv2_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/mobilenetv2_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh mobilenetv2_baseline_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv2_resa_culane.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv2_resa_culane_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/resa/mobilenetv2_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/mobilenetv2_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh mobilenetv2_resa_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv2_resa_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv2_resa_tusimple_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/resa/mobilenetv2_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/mobilenetv2_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh mobilenetv2_resa_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv3-large_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv3-large_baseline_culane_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/mobilenetv3_large_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/mobilenetv3_large_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh mobilenetv3-large_baseline_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv3-large_baseline_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv3-large_baseline_tusimple_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/mobilenetv3_large_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/mobilenetv3_large_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh mobilenetv3-large_baseline_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv3-large_resa_culane.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv3-large_resa_culane_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/resa/mobilenetv3_large_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/mobilenetv3_large_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh mobilenetv3-large_resa_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/mobilenetv3-large_resa_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: mobilenetv3-large_resa_tusimple_20220209.pt
# Training
python main_landet.py --train --config=configs/lane_detection/resa/mobilenetv3_large_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/mobilenetv3_large_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh mobilenetv3-large_resa_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/repvgg-a0_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: repvgg-a0_baseline_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/baseline/repvgg_a0_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/baseline/repvgg_a0_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-a0_baseline_culane test

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/repvgg-a1_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: repvgg-a1_baseline_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/baseline/repvgg_a1_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/baseline/repvgg_a1_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-a1_baseline_culane test

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/repvgg-a1_scnn_culane.sh Normal file
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#!/bin/bash
# Trained weights: repvgg-a1_scnn_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/scnn/repvgg_a1_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/scnn/repvgg_a1_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-a1_scnn_culane test

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/repvgg-b0_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: repvgg-b0_baseline_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/baseline/repvgg_b0_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/baseline/repvgg_b0_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-b0_baseline_culane test

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/repvgg-b1g2_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: repvgg-b1g2_baseline_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/baseline/repvgg_b1g2_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/baseline/repvgg_b1g2_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-b1g2_baseline_culane test

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#!/bin/bash
# Trained weights: repvgg-b2_baseline_culane_20220112.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --mixed-precision --config configs/lane_detection/baseline/repvgg_b2_culane.py
# Predicting lane points for testing
python main_landet.py --test --config configs/lane_detection/baseline/repvgg_b2_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh repvgg-b2_baseline_culane test

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#!/bin/bash
# Trained weights: resnet101_baseline_culane_20210312.pt
# Training, scale lr linearly on 11G GPU (square root scaling does not converge on this dataset)
python main_landet.py --train --config=configs/lane_detection/baseline/resnet101_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet101_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet101_baseline_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet101_baseline_tusimple_20210424.pt
# Training, scale lr by square root on 11G GPU
python main_landet.py --train --config=configs/lane_detection/baseline/resnet101_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet101_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet101_baseline_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet101_resa_culane_20211016.pt
# Training
python -m torch.distributed.launch --nproc_per_node=8 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet101_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet101_culane.py
# Testing with official scripts
./autotest_culane.sh resnet101_resa_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet101_resa_tusimple_20211019.pt
# Training
python -m torch.distributed.launch --nproc_per_node=8 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet101_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet101_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh resnet101_resa_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet101_scnn_culane_20210314.pt
# Training, scale lr linearly on 11G GPU (square root scaling does not converge on this dataset)
python main_landet.py --train --config=configs/lane_detection/scnn/resnet101_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet101_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet101_scnn_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet101_scnn_tusimple_20210218.pt
# Training, scale lr by square root on 11G GPU
python main_landet.py --train --config=configs/lane_detection/scnn/resnet101_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet101_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet101_scnn_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet18_baseline_culane_20210222.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/resnet18_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet18_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet18_baseline_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet18_baseline_tusimple_20210215.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/resnet18_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet18_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet18_baseline_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet18_bezierlanenet_culane-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_culane_aug1b.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_culane_aug1b.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet18_bezierlanenet_culane-aug1b test checkpoints

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#!/bin/bash
# Trained weights: resnet18_bezierlanenet_llamas-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_llamas_aug1b.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/bezierlanenet/resnet18_llamas_aug1b.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh resnet18_bezierlanenet_llamas-aug1b val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_llamas_aug1b.py --mixed-precision

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#!/bin/bash
# Trained weights: resnet18_bezierlanenet_tusimple-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet18_tusimple_aug1b.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet18_tusimple_aug1b.py
# Testing with official scripts
./autotest_tusimple.sh resnet18_bezierlanenet_tusimple-aug1b test checkpoints

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#!/bin/bash
# Trained weights: resnet18_laneatt_culane_20220225.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/laneatt/resnet18_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/laneatt/resnet18_culane.py
# Testing with official scripts
./autotest_culane.sh resnet18_laneatt_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet18_resa_culane_20211016.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet18_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet18_culane.py
# Testing with official scripts
./autotest_culane.sh resnet18_resa_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet18_resa_tusimple_20211019.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet18_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet18_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh resnet18_resa_tusimple test checkpoints

8
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#!/bin/bash
# Trained weights: resnet18_scnn_culane_20210222.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/resnet18_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet18_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet18_scnn_culane test checkpoints

8
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#!/bin/bash
# Trained weights: resnet18_scnn_tusimple_20210424.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/resnet18_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet18_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet18_scnn_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet18s_lstr-aug_culane_20210721.pt
# Training
python main_landet.py --train --config=configs/lane_detection/lstr/resnet18s_culane_aug.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/lstr/resnet18s_culane_aug.py
# Testing with official scripts
./autotest_culane.sh resnet18s_lstr-aug_culane test checkpoints

8
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#!/bin/bash
# Trained weights: resnet18s_lstr-aug_tusimple_20210629.pt
# Training
python main_landet.py --train --config=configs/lane_detection/lstr/resnet18s_tusimple_aug.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/lstr/resnet18s_tusimple_aug.py
# Testing with official scripts
./autotest_tusimple.sh resnet18s_lstr-aug_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet18s_lstr_culane_20210722.pt
# Training
python main_landet.py --train --config=configs/lane_detection/lstr/resnet18s_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/lstr/resnet18s_culane.py
# Testing with official scripts
./autotest_culane.sh resnet18s_lstr_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet18s_lstr_tusimple_20210701.pt
# Training
python main_landet.py --train --config=configs/lane_detection/lstr/resnet18s_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/lstr/resnet18s_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh resnet18s_lstr_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet34_baseline-aug_tusimple_20210723.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/baseline/resnet34_tusimple_aug.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet34_tusimple_aug.py --mixed-precision
# Testing with official scripts
./autotest_tusimple-aug.sh resnet34_baseline_tusimple-aug test checkpoints

8
algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_baseline_culane_20210219.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/resnet34_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet34_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet34_baseline_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet34_baseline_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/resnet34_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/baseline/resnet34_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh resnet34_baseline_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/baseline/resnet34_llamas.py --mixed-precision

8
algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_baseline_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_baseline_tusimple_20210424.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/resnet34_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet34_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet34_baseline_tusimple test checkpoints

8
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#!/bin/bash
# Trained weights: resnet34_bezierlanenet_culane-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet34_culane_aug1b.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet34_culane_aug1b.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet34_bezierlanenet_culane-aug1b test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_bezierlanenet_llamas-aug1b.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_bezierlanenet_llamas-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet34_llamas_aug1b.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/bezierlanenet/resnet34_llamas_aug1b.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh resnet34_bezierlanenet_llamas-aug1b val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet34_llamas_aug1b.py --mixed-precision

8
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#!/bin/bash
# Trained weights: resnet34_bezierlanenet_tusimple-aug1b_20211109.pt
# Training
python main_landet.py --train --config=configs/lane_detection/bezierlanenet/resnet34_tusimple_aug1b.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/bezierlanenet/resnet34_tusimple_aug1b.py
# Testing with official scripts
./autotest_tusimple.sh resnet34_bezierlanenet_tusimple-aug1b test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_laneatt_culane.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_laneatt_culane_20220320.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/laneatt/resnet34_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/laneatt/resnet34_culane.py
# Testing with official scripts
./autotest_culane.sh resnet34_laneatt_culane test checkpoints

8
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#!/bin/bash
# Trained weights: resnet34_lstr-aug_culane_20211012.pt
# Training
python main_landet.py --train --config=configs/lane_detection/lstr/resnet34_culane_aug.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/lstr/resnet34_culane_aug.py
# Testing with official scripts
./autotest_culane.sh resnet34_lstr_culane-aug test checkpoints

8
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#!/bin/bash
# Trained weights: resnet34_resa_culane_20211016.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet34_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet34_culane.py
# Testing with official scripts
./autotest_culane.sh resnet34_resa_culane test checkpoints

8
algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_resa_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_resa_tusimple_20211019.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet34_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet34_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh resnet34_resa_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet34_scnn-aug_tusimple_20210723.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landet.py --train --config=configs/lane_detection/scnn/resnet34_tusimple_aug.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet34_tusimple_aug.py --mixed-precision
# Testing with official scripts
./autotest_tusimple-aug.sh resnet34_scnn_tusimple-aug test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet34_scnn_culane.sh Normal file
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#!/bin/bash
# Trained weights: resnet34_scnn_culane_20210220.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/resnet34_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet34_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet34_scnn_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet34_scnn_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/resnet34_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/scnn/resnet34_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh resnet34_scnn_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/scnn/resnet34_llamas.py --mixed-precision

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#!/bin/bash
# Trained weights: resnet34_scnn_tusimple_20210424.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/resnet34_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet34_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet34_scnn_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet50_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: resnet50_baseline_culane_20210308.pt
# Training, scale lr linearly on 11G GPU (square root scaling does not converge on this dataset)
python main_landet.py --train --config=configs/lane_detection/baseline/resnet50_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet50_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet50_baseline_culane test checkpoints

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#!/bin/bash
# Trained weights: resnet50_baseline_tusimple_20210217.pt
# Training, scale lr by square root on 11G GPU
python main_landet.py --train --config=configs/lane_detection/baseline/resnet50_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/resnet50_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet50_baseline_tusimple test checkpoints

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#!/bin/bash
# Trained weights: resnet50_resa_culane_20211016.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet50_culane.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet50_culane.py
# Testing with official scripts
./autotest_culane.sh resnet50_resa_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet50_resa_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: resnet50_resa_tusimple_20211019.pt
# Training
python -m torch.distributed.launch --nproc_per_node=4 --use_env main_landet.py --train --config=configs/lane_detection/resa/resnet50_tusimple.py
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/resa/resnet50_tusimple.py
# Testing with official scripts
./autotest_tusimple.sh resnet50_resa_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet50_scnn_culane.sh Normal file
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#!/bin/bash
# Trained weights: resnet50_scnn_culane_20210311.pt
# Training, scale lr linearly on 11G GPU (square root scaling does not converge on this dataset)
python main_landet.py --train --config=configs/lane_detection/scnn/resnet50_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet50_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh resnet50_scnn_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/resnet50_scnn_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: resnet50_scnn_tusimple_20210217.pt
# Training, scale lr by square root on 11G GPU
python main_landet.py --train --config=configs/lane_detection/scnn/resnet50_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/resnet50_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh resnet50_scnn_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/swin-tiny_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: swin-tiny_baseline_culane_20220119.pt
# Training
python -m torch.distributed.launch --nproc_per_node=2 --use_env main_landec.py --train --config=configs/lane_detection/baseline/swin_tiny_culane.py
# Predicting lane points for testing
python main_landec.py --test --config=configs/lane_detection/baseline/swin_tiny_culane.py
# Testing with official scripts
./autotest_culane.sh swin-tiny_baseline_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/vgg16_baseline_culane.sh Normal file
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#!/bin/bash
# Trained weights: vgg16_baseline_culane_20210309.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/vgg16_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/vgg16_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh vgg16_baseline_culane test checkpoints

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#!/bin/bash
# Trained weights: vgg16_baseline_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/vgg16_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/baseline/vgg16_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh vgg16_baseline_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/baseline/vgg16_llamas.py --mixed-precision

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#!/bin/bash
# Trained weights: vgg16_baseline_tusimple_20210223.pt
# Training
python main_landet.py --train --config=configs/lane_detection/baseline/vgg16_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/baseline/vgg16_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh vgg16_baseline_tusimple test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/vgg16_scnn_culane.sh Normal file
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#!/bin/bash
# Trained weights: vgg16_scnn_culane_20210309.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/vgg16_culane.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/vgg16_culane.py --mixed-precision
# Testing with official scripts
./autotest_culane.sh vgg16_scnn_culane test checkpoints

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/vgg16_scnn_llamas.sh Normal file
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#!/bin/bash
# Trained weights: vgg16_scnn_llamas_20210625.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/vgg16_llamas.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --val --config=configs/lane_detection/scnn/vgg16_llamas.py --mixed-precision
# Testing with official scripts
./autotest_llamas.sh vgg16_scnn_llamas val checkpoints
# Predict lane points for the eval server, find results in ./output
python main_landet.py --test --config=configs/lane_detection/scnn/vgg16_llamas.py --mixed-precision

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algorithms/lane_ufld/code/pytorch-auto-drive-master/tools/shells/vgg16_scnn_tusimple.sh Normal file
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#!/bin/bash
# Trained weights: vgg16_scnn_tusimple_20210224.pt
# Training
python main_landet.py --train --config=configs/lane_detection/scnn/vgg16_tusimple.py --mixed-precision
# Predicting lane points for testing
python main_landet.py --test --config=configs/lane_detection/scnn/vgg16_tusimple.py --mixed-precision
# Testing with official scripts
./autotest_tusimple.sh vgg16_scnn_tusimple test checkpoints