【ppyoloe+】19届智能车完全模型组非官方基线

【智能车】从零写一份自己的完全模型智能车寻路算法(有手就行) --- 01 智能车完全模型寻路算法01,目前更新完图像处理。 阅读详情

基于十九届智能车百度完全模型组线上赛baseline修改

调整参数最高能到0.989吧

一、环境准备

1.安装PaddleDetection

In [1]

# 解压PaddleDetection压缩包
%cd /home/aistudio/data/data267567
!unzip -q PaddleDetection-release-2.6.zip -d /home/aistudio
/home/aistudio/data/data267567

2.安装依赖

In [2]

# 安装requirements中的依赖
%cd ~/PaddleDetection-release-2.6
!pip install -r requirements.txt
#安装过慢可以打开requirements.txt文件,注释掉opencv-python <= 4.6.0(在前面加“#”)
# 然后将此命令前的“#”删掉重新运行
!pip install opencv-python <= 4.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

# 编译安装paddledet
!python setup.py install
%cd ~
copying ppdet/modeling/reid/resnet.py -> build/lib/ppdet/modeling/reid
copying ppdet/modeling/reid/resnet_embedding.py -> build/lib/ppdet/modeling/reid
copying ppdet/modeling/reid/jde_embedding_head.py -> build/lib/ppdet/modeling/reid
creating build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/convnext.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/esnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/hardnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/cspresnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/vgg.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/dla.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/lcnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/ghostnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/csp_darknet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/transformer_utils.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/lite_hrnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobilenet_v1.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/senet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/hrnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/swin_transformer.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/vision_transformer.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobileone.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/name_adapter.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/shufflenet_v2.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/__init__.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/resnet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/trans_encoder.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/darknet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/blazenet.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/res2net.py -> build/lib/ppdet/modeling/backbones
copying ppdet/modeling/backbones/mobilenet_v3.py -> build/lib/ppdet/modeling/backbones
creating build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/utils.py -> build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/__init__.py -> build/lib/ppdet/modeling/ssod
copying ppdet/modeling/ssod/losses.py -> build/lib/ppdet/modeling/ssod
creating build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/solov2_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/bbox_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ttf_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/simota_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/pico_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/gfl_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/yolo_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/centertrack_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/face_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/roi_extractor.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/fcosr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_r_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/sparse_roi_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/centernet_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/keypoint_hrhrnet_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/petr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/retina_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/__init__.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/sparsercnn_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ssd_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/cascade_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/tood_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/yolof_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/fcos_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/detr_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/ppyoloe_contrast_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/mask_head.py -> build/lib/ppdet/modeling/heads
copying ppdet/modeling/heads/s2anet_head.py -> build/lib/ppdet/modeling/heads
creating build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/proposal_generator.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/anchor_generator.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/embedding_rpn_head.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/target.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/rpn_head.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/__init__.py -> build/lib/ppdet/modeling/proposal_generator
copying ppdet/modeling/proposal_generator/target_layer.py -> build/lib/ppdet/modeling/proposal_generator
creating build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/ocsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/base_jde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/center_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/jde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/__init__.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/botsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/deepsort_tracker.py -> build/lib/ppdet/modeling/mot/tracker
copying ppdet/modeling/mot/tracker/base_sde_tracker.py -> build/lib/ppdet/modeling/mot/tracker
creating build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/deepsort_matching.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/ocsort_matching.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/__init__.py -> build/lib/ppdet/modeling/mot/matching
copying ppdet/modeling/mot/matching/jde_matching.py -> build/lib/ppdet/modeling/mot/matching
creating build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/gmc.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/kalman_filter.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/ocsort_kalman_filter.py -> build/lib/ppdet/modeling/mot/motion
copying ppdet/modeling/mot/motion/__init__.py -> build/lib/ppdet/modeling/mot/motion
creating build/lib/ppdet/core/config
copying ppdet/core/config/yaml_helpers.py -> build/lib/ppdet/core/config
copying ppdet/core/config/schema.py -> build/lib/ppdet/core/config
copying ppdet/core/config/__init__.py -> build/lib/ppdet/core/config
creating build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/test_get_model.py -> build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/test_list_model.py -> build/lib/ppdet/model_zoo/tests
copying ppdet/model_zoo/tests/__init__.py -> build/lib/ppdet/model_zoo/tests
creating build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/__init__.py -> build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/chip_box_utils.py -> build/lib/ppdet/data/crop_utils
copying ppdet/data/crop_utils/annotation_cropper.py -> build/lib/ppdet/data/crop_utils
creating build/lib/ppdet/data/source
copying ppdet/data/source/mot.py -> build/lib/ppdet/data/source
copying ppdet/data/source/category.py -> build/lib/ppdet/data/source
copying ppdet/data/source/sniper_coco.py -> build/lib/ppdet/data/source
copying ppdet/data/source/keypoint_coco.py -> build/lib/ppdet/data/source
copying ppdet/data/source/dataset.py -> build/lib/ppdet/data/source
copying ppdet/data/source/voc.py -> build/lib/ppdet/data/source
copying ppdet/data/source/__init__.py -> build/lib/ppdet/data/source
copying ppdet/data/source/pose3d_cmb.py -> build/lib/ppdet/data/source
copying ppdet/data/source/widerface.py -> build/lib/ppdet/data/source
copying ppdet/data/source/coco.py -> build/lib/ppdet/data/source
creating build/lib/ppdet/data/transform
copying ppdet/data/transform/autoaugment_utils.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/op_helper.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/atss_assigner.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/mot_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/batch_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/gridmask_utils.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/rotated_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/keypoint_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/keypoints_3d_operators.py -> build/lib/ppdet/data/transform
copying ppdet/data/transform/__init__.py -> build/lib/ppdet/data/transform
copying ppdet/model_zoo/MODEL_ZOO -> build/lib/ppdet/model_zoo
creating build/bdist.linux-x86_64
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/ppdet
copying build/lib/ppdet/version.py -> build/bdist.linux-x86_64/egg/ppdet
creating build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolo.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/pose3d_metro.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_hrhrnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/detr.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/queryinst.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/picodet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/gfl.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/fcos.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/mask_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/bytetrack.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/jde.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/sparse_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/meta_arch.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_hrnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/deepsort.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/s2anet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolox.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/retinanet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/solov2.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/tood.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/centertrack.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/blazeface.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/yolof.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/fairmot.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/centernet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/faster_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/keypoint_petr.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ppyoloe.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/cascade_rcnn.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ssd.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/architectures/ttfnet.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/architectures
copying build/lib/ppdet/modeling/post_process.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
copying build/lib/ppdet/modeling/ops.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/ocsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/base_jde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/center_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/jde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/botsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/deepsort_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/tracker/base_sde_tracker.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker
copying build/lib/ppdet/modeling/mot/utils.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/deepsort_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/ocsort_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/matching/jde_matching.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching
copying build/lib/ppdet/modeling/mot/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
creating build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/gmc.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/kalman_filter.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/ocsort_kalman_filter.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/motion/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion
copying build/lib/ppdet/modeling/mot/visualization.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/mot
copying build/lib/ppdet/modeling/shape_spec.py -> build/bdist.linux-x86_64/egg/ppdet/modeling
creating build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/varifocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/smooth_l1_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/gfocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/focal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/jde_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/probiou_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/iou_aware_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/cot_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/iou_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/yolo_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/ssd_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/pose3d_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/queryinst_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/supcontrast.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/fairmot_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/sparsercnn_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/detr_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/fcos_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/ctfocal_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/keypoint_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
copying build/lib/ppdet/modeling/losses/solov2_loss.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/losses
creating build/bdist.linux-x86_64/egg/ppdet/modeling/necks
copying build/lib/ppdet/modeling/necks/dilated_encoder.py -> build/bdist.linux-x86_64/egg/ppdet/modeling/necks
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copying build/lib/ppdet/data/crop_utils/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data/crop_utils
copying build/lib/ppdet/data/crop_utils/chip_box_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data/crop_utils
copying build/lib/ppdet/data/crop_utils/annotation_cropper.py -> build/bdist.linux-x86_64/egg/ppdet/data/crop_utils
copying build/lib/ppdet/data/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data
creating build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/mot.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/category.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/sniper_coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/keypoint_coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/dataset.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/voc.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/pose3d_cmb.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/widerface.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/source/coco.py -> build/bdist.linux-x86_64/egg/ppdet/data/source
copying build/lib/ppdet/data/shm_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data
creating build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/autoaugment_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/op_helper.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/atss_assigner.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/mot_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/batch_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/gridmask_utils.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/rotated_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/keypoint_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/keypoints_3d_operators.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
copying build/lib/ppdet/data/transform/__init__.py -> build/bdist.linux-x86_64/egg/ppdet/data/transform
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker/ocsort_tracker.py to ocsort_tracker.cpython-37.pyc
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker/jde_tracker.py to jde_tracker.cpython-37.pyc
byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker/__init__.py to __init__.cpython-37.pyc
byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker/botsort_tracker.py to botsort_tracker.cpython-37.pyc
byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/tracker/deepsort_tracker.py to deepsort_tracker.cpython-37.pyc
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/utils.py to utils.cpython-37.pyc
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/matching/__init__.py to __init__.cpython-37.pyc
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byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/mot/motion/ocsort_kalman_filter.py to ocsort_kalman_filter.cpython-37.pyc
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creating build/bdist.linux-x86_64/egg/EGG-INFO
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ppdet.data.transform.__pycache__.autoaugment_utils.cpython-37: module MAY be using inspect.stack
ppdet.modeling.tests.__pycache__.test_mstest.cpython-37: module references __file__
ppdet.modeling.tests.__pycache__.test_ops.cpython-37: module references __file__
ppdet.modeling.tests.__pycache__.test_yolov3_loss.cpython-37: module references __file__
creating dist
creating 'dist/paddledet-2.6.0-py3.7.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing paddledet-2.6.0-py3.7.egg
creating /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddledet-2.6.0-py3.7.egg
Extracting paddledet-2.6.0-py3.7.egg to /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Installed /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddledet-2.6.0-py3.7.egg
Processing dependencies for paddledet==2.6.0
Searching for pyclipper==1.3.0.post5
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: setuptools 56.2.0
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Adding scipy 1.6.3 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Best match: shapely 2.0.4
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Best match: PyYAML 5.1.2
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Best match: opencv-python 4.1.1.26
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for scikit-learn==0.24.2
Best match: scikit-learn 0.24.2
Adding scikit-learn 0.24.2 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
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Adding xmltodict 0.13.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pandas==1.1.5
Best match: pandas 1.1.5
Adding pandas 1.1.5 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for matplotlib==2.2.3
Best match: matplotlib 2.2.3
Adding matplotlib 2.2.3 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for protobuf==3.20.0
Best match: protobuf 3.20.0
Adding protobuf 3.20.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Flask==1.1.1
Best match: Flask 1.1.1
Adding Flask 1.1.1 to easy-install.pth file
Installing flask script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for six==1.16.0
Best match: six 1.16.0
Adding six 1.16.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Pillow==8.2.0
Best match: Pillow 8.2.0
Adding Pillow 8.2.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for bce-python-sdk==0.8.53
Best match: bce-python-sdk 0.8.53
Adding bce-python-sdk 0.8.53 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for requests==2.24.0
Best match: requests 2.24.0
Adding requests 2.24.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Flask-Babel==1.0.0
Best match: Flask-Babel 1.0.0
Adding Flask-Babel 1.0.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for importlib-metadata==4.2.0
Best match: importlib-metadata 4.2.0
Adding importlib-metadata 4.2.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for typing-extensions==4.7.1
Best match: typing-extensions 4.7.1
Adding typing-extensions 4.7.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for joblib==0.14.1
Best match: joblib 0.14.1
Adding joblib 0.14.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for threadpoolctl==2.1.0
Best match: threadpoolctl 2.1.0
Adding threadpoolctl 2.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for python-dateutil==2.8.2
Best match: python-dateutil 2.8.2
Adding python-dateutil 2.8.2 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pytz==2019.3
Best match: pytz 2019.3
Adding pytz 2019.3 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pyparsing==3.0.9
Best match: pyparsing 3.0.9
Adding pyparsing 3.0.9 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for cycler==0.10.0
Best match: cycler 0.10.0
Adding cycler 0.10.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for kiwisolver==1.1.0
Best match: kiwisolver 1.1.0
Adding kiwisolver 1.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Click==7.0
Best match: Click 7.0
Adding Click 7.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for itsdangerous==1.1.0
Best match: itsdangerous 1.1.0
Adding itsdangerous 1.1.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Werkzeug==0.16.0
Best match: Werkzeug 0.16.0
Adding Werkzeug 0.16.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Jinja2==3.0.0
Best match: Jinja2 3.0.0
Adding Jinja2 3.0.0 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for future==0.18.0
Best match: future 0.18.0
Adding future 0.18.0 to easy-install.pth file
Installing futurize script to /opt/conda/envs/python35-paddle120-env/bin
Installing pasteurize script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for pycryptodome==3.9.9
Best match: pycryptodome 3.9.9
Adding pycryptodome 3.9.9 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for idna==2.8
Best match: idna 2.8
Adding idna 2.8 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for certifi==2019.9.11
Best match: certifi 2019.9.11
Adding certifi 2019.9.11 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for chardet==3.0.4
Best match: chardet 3.0.4
Adding chardet 3.0.4 to easy-install.pth file
Installing chardetect script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for urllib3==1.25.6
Best match: urllib3 1.25.6
Adding urllib3 1.25.6 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for Babel==2.8.0
Best match: Babel 2.8.0
Adding Babel 2.8.0 to easy-install.pth file
Installing pybabel script to /opt/conda/envs/python35-paddle120-env/bin

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for zipp==3.8.1
Best match: zipp 3.8.1
Adding zipp 3.8.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Searching for MarkupSafe==2.0.1
Best match: MarkupSafe 2.0.1
Adding MarkupSafe 2.0.1 to easy-install.pth file

Using /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages
Finished processing dependencies for paddledet==2.6.0
/home/aistudio

二、数据准备

1.解压官方数据集压缩包

In [3]

# 移动到当前挂载的数据集目录
# 解压数据集到指定目录下
%cd /home/aistudio/data/data257994
!unzip -q Car2024.zip -d /home/aistudio
/home/aistudio/data/data257994

2.生成训练所需文件

这里训练集和验证集是按4:1划分的,可以将全部的数据集都用作训练

In [4]

import os

#生成train.txt、val.txt
xml_dir  = '/home/aistudio/Car2024/annotations'
img_dir = '/home/aistudio/Car2024/images'
path_list = list()
for img in os.listdir(img_dir):
    img_path = os.path.join(img_dir,img)
    xml_path = os.path.join(xml_dir,img.replace('jpg', 'xml'))
    path_list.append((img_path, xml_path))

train_f = open('/home/aistudio/Car2024/train.txt','w') 
val_f = open('/home/aistudio/Car2024/val.txt','w') 

for i ,content in enumerate(path_list):
    img, xml = content
    text = img + ' ' + xml + '\n'
    if i % 5 == 0:
        val_f.write(text)
    else:
        train_f.write(text)
train_f.close()
val_f.close()

In [5]


%cd ~/PaddleDetection-release-2.6
# 生成训练集
!python tools/x2coco.py \
        --dataset_type voc \
        --voc_anno_dir /home/aistudio/Car2024/annotations/ \
        --voc_anno_list /home/aistudio/Car2024/train.txt \
        --voc_label_list /home/aistudio/Car2024/label_list.txt \
        --voc_out_name /home/aistudio/Car2024/train.json

# 生成验证集
!python tools/x2coco.py \
        --dataset_type voc \
        --voc_anno_dir /home/aistudio/Car2024/annotations/ \
        --voc_anno_list /home/aistudio/Car2024/val.txt \
        --voc_label_list /home/aistudio/Car2024/label_list.txt \
        --voc_out_name /home/aistudio/Car2024/val.json
/home/aistudio/PaddleDetection-release-2.6
Start converting !
100%|████████████████████████████████████| 1655/1655 [00:00<00:00, 14389.39it/s]
Start converting !
100%|██████████████████████████████████████| 414/414 [00:00<00:00, 12318.42it/s]

三、数据分析

1.标签数量统计

In [6]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def count_num(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')

    dict = {} # 新建字典,用于存放各类标签名及其对应的数目
    for i, file in enumerate(annotations): # 遍历xml文件
       
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()

        # 遍历文件的所有标签
        for obj in root.iter('object'):
            name = obj.find('name').text
            if(name in dict.keys()): dict[name] += 1 # 如果标签不是第一次出现,则+1
            else: dict[name] = 1 # 如果标签是第一次出现,则将该标签名对应的value初始化为1

    # 打印结果
    print("各类标签的数量分别为:")
    for key in dict.keys(): 
        print(key + ': ' + str(dict[key]))            

indir='/home/aistudio/Car2024/annotations'   # xml文件所在的目录
count_num(indir) # 调用函数统计各类标签数目
各类标签的数量分别为:
block: 315
cone: 4942
patient: 176
bridge: 209
bomb: 212
safety: 198
evil: 94
prop: 405
spy: 23
danger: 176
crosswalk: 62
tumble: 129
thief: 37

2.标注框高宽比分析

第一次运行卡了,可以尝试重新运行一次,下面应该是要有图的

In [8]

import os
import matplotlib.pyplot as plt
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def ratio(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    # count_0, count_1, count_2, count_3 = 0, 0, 0, 0 # 举反例,不要这么写
    count = [0 for i in range(20)]

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()

        # 遍历文件的所有检测框
        for obj in root.iter('object'):
            xmin = obj.find('bndbox').find('xmin').text
            ymin = obj.find('bndbox').find('ymin').text
            xmax = obj.find('bndbox').find('xmax').text
            ymax = obj.find('bndbox').find('ymax').text
            Aspect_ratio = (int(ymax)-int(ymin)) / (int(xmax)-int(xmin))
            if int(Aspect_ratio/0.25) < 19:
                count[int(Aspect_ratio/0.25)] += 1
            else:
                count[-1] += 1
    sign = [0.25*i for i in range(20)]
    plt.bar(x=sign, height=count)
    print(count)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
ratio(indir)
[52, 187, 877, 846, 1999, 2089, 548, 190, 99, 39, 30, 13, 5, 3, 0, 0, 1, 0, 0, 0]

<Figure size 640x480 with 1 Axes>

3.图像尺寸分析

In [9]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def Image_size(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    width_heights = []

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()
        width = int(root.find('size').find('width').text)
        height = int(root.find('size').find('height').text)
        if [width, height] not in width_heights: width_heights.append([width, height])
    print("数据集中,有{}种不同的尺寸,分别是:".format(len(width_heights)))
    for item in width_heights:
        print(item)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
Image_size(indir)
数据集中,有1种不同的尺寸,分别是:
[320, 240]

4.检测框中心分布分析

In [10]

import os
from unicodedata import name
import xml.etree.ElementTree as ET
import glob

def distribution(indir):
    # 提取xml文件列表
    os.chdir(indir)
    annotations = os.listdir('.')
    annotations = glob.glob(str(annotations) + '*.xml')
    data_x, data_y = [], []

    for i, file in enumerate(annotations): # 遍历xml文件
        # actual parsing
        in_file = open(file, encoding = 'utf-8')
        tree = ET.parse(in_file)
        root = tree.getroot()
        width = int(root.find('size').find('width').text)
        height = int(root.find('size').find('height').text)

        # 遍历文件的所有检测框
        for obj in root.iter('object'):
            xmin = int(obj.find('bndbox').find('xmin').text)
            ymin = int(obj.find('bndbox').find('ymin').text)
            xmax = int(obj.find('bndbox').find('xmax').text)
            ymax = int(obj.find('bndbox').find('ymax').text)
            x = (xmin + (xmax-xmin)) / width
            y = (ymin + (ymax-ymin)) / height
            data_x.append(x)
            data_y.append(y)
            
    plt.scatter(data_x, data_y, s=1, alpha=0.1)

indir='/home/aistudio/Car2024/annotations/'   # xml文件所在的目录
distribution(indir)

<Figure size 640x480 with 1 Axes>

四、模型训练

1.修改数据集配置文件

修改模型配置文件中的第一行,将ppyoloe_plus_crn_m_80e_coco.yml第一行的coco_detection.yml修改为coco_newcar.yml

In [11]

%cd /home/aistudio
!cp coco_newcar.yml /home/aistudio/PaddleDetection-release-2.6/configs/datasets/
/home/aistudio

2.修改模型配置文件参数

具体参数需自行调整,默认使用官方的,建议调整下base_lr,由于我们的单卡,可以将默认的数值改为原先的1/8

3.模型训练

这里采用的模型是ppyoloe+_m

In [12]


%cd /home/aistudio/PaddleDetection-release-2.6
# 模型训练 
# 如果要恢复训练,则加上 -r output/ppyoloe_plus_crn_m_80e_coco/best_model
# 如果要边训练边评估,则加上--eval
# 训练时的日志输出将保存在--vdl_log_dir所指的路径下
# 模型压缩则加上--slim_config configs/slim/xxx/{SLIM_CONFIG.yml}({SLIM_CONFIG.yml}为指定压缩策略配置文件)
!python tools/train.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml --eval \
    --use_vdl=true \
    --vdl_log_dir=VisualDL
/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
[06/01 12:27:00] ppdet.data.source.coco INFO: Load [1655 samples valid, 0 samples invalid] in file /home/aistudio/Car2024/train.json.
W0601 12:27:00.946624  1254 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.0, Runtime API Version: 11.2
W0601 12:27:00.954115  1254 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
[06/01 12:27:05] ppdet.utils.download INFO: Downloading ppyoloe_crn_m_obj365_pretrained.pdparams from https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
100%|█████████████████████████████████| 104604/104604 [00:10<00:00, 9832.91KB/s]
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.0.bias is unmatched with the shape [13] in model yolo_head.pred_cls.0.bias. And the weight yolo_head.pred_cls.0.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 576, 3, 3] in pretrained weight yolo_head.pred_cls.0.weight is unmatched with the shape [13, 576, 3, 3] in model yolo_head.pred_cls.0.weight. And the weight yolo_head.pred_cls.0.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.1.bias is unmatched with the shape [13] in model yolo_head.pred_cls.1.bias. And the weight yolo_head.pred_cls.1.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 288, 3, 3] in pretrained weight yolo_head.pred_cls.1.weight is unmatched with the shape [13, 288, 3, 3] in model yolo_head.pred_cls.1.weight. And the weight yolo_head.pred_cls.1.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.2.bias is unmatched with the shape [13] in model yolo_head.pred_cls.2.bias. And the weight yolo_head.pred_cls.2.bias will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: The shape [365, 144, 3, 3] in pretrained weight yolo_head.pred_cls.2.weight is unmatched with the shape [13, 144, 3, 3] in model yolo_head.pred_cls.2.weight. And the weight yolo_head.pred_cls.2.weight will not be loaded
[06/01 12:27:16] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/ppyoloe_crn_m_obj365_pretrained.pdparams
[06/01 12:27:20] ppdet.engine INFO: Epoch: [0] [  0/206] learning_rate: 0.000000 loss: 4.613988 loss_cls: 3.654145 loss_iou: 0.210726 loss_dfl: 0.866056 loss_l1: 0.385787 eta: 17:34:44 batch_cost: 3.8401 data_cost: 0.0005 ips: 2.0833 images/s
[06/01 12:28:30] ppdet.engine INFO: Epoch: [0] [100/206] learning_rate: 0.000097 loss: 3.047830 loss_cls: 2.266012 loss_iou: 0.168912 loss_dfl: 0.721105 loss_l1: 0.231601 eta: 2:34:58 batch_cost: 0.5350 data_cost: 0.0003 ips: 14.9541 images/s
[06/01 12:29:41] ppdet.engine INFO: Epoch: [0] [200/206] learning_rate: 0.000194 loss: 2.323405 loss_cls: 1.533948 loss_iou: 0.158725 loss_dfl: 0.670796 loss_l1: 0.194519 eta: 2:30:20 batch_cost: 0.5404 data_cost: 0.0003 ips: 14.8045 images/s
[06/01 12:29:47] ppdet.engine INFO: Epoch: [1] [  0/206] learning_rate: 0.000200 loss: 2.257758 loss_cls: 1.521657 loss_iou: 0.158959 loss_dfl: 0.669803 loss_l1: 0.195281 eta: 2:32:04 batch_cost: 0.5521 data_cost: 0.0098 ips: 14.4891 images/s
[06/01 12:30:59] ppdet.engine INFO: Epoch: [1] [100/206] learning_rate: 0.000297 loss: 2.089173 loss_cls: 1.335514 loss_iou: 0.161354 loss_dfl: 0.688102 loss_l1: 0.199043 eta: 2:30:42 batch_cost: 0.5558 data_cost: 0.0003 ips: 14.3943 images/s
[06/01 12:32:10] ppdet.engine INFO: Epoch: [1] [200/206] learning_rate: 0.000394 loss: 1.935451 loss_cls: 1.187285 loss_iou: 0.156842 loss_dfl: 0.679105 loss_l1: 0.195983 eta: 2:29:01 batch_cost: 0.5476 data_cost: 0.0003 ips: 14.6086 images/s
[06/01 12:32:17] ppdet.engine INFO: Epoch: [2] [  0/206] learning_rate: 0.000400 loss: 1.921179 loss_cls: 1.183878 loss_iou: 0.156247 loss_dfl: 0.673825 loss_l1: 0.190812 eta: 2:29:47 batch_cost: 0.5597 data_cost: 0.0088 ips: 14.2935 images/s
[06/01 12:33:28] ppdet.engine INFO: Epoch: [2] [100/206] learning_rate: 0.000497 loss: 2.121108 loss_cls: 1.389727 loss_iou: 0.166188 loss_dfl: 0.685305 loss_l1: 0.205390 eta: 2:28:00 batch_cost: 0.5429 data_cost: 0.0003 ips: 14.7344 images/s
[06/01 12:34:40] ppdet.engine INFO: Epoch: [2] [200/206] learning_rate: 0.000594 loss: 2.134946 loss_cls: 1.317788 loss_iou: 0.178044 loss_dfl: 0.697273 loss_l1: 0.206171 eta: 2:27:04 batch_cost: 0.5560 data_cost: 0.0004 ips: 14.3877 images/s
[06/01 12:34:46] ppdet.engine INFO: Epoch: [3] [  0/206] learning_rate: 0.000600 loss: 2.152541 loss_cls: 1.326942 loss_iou: 0.179959 loss_dfl: 0.698840 loss_l1: 0.206171 eta: 2:27:36 batch_cost: 0.5719 data_cost: 0.0070 ips: 13.9893 images/s
[06/01 12:36:01] ppdet.engine INFO: Epoch: [3] [100/206] learning_rate: 0.000697 loss: 1.838928 loss_cls: 1.065280 loss_iou: 0.162629 loss_dfl: 0.671139 loss_l1: 0.188668 eta: 2:27:09 batch_cost: 0.5717 data_cost: 0.0003 ips: 13.9929 images/s
[06/01 12:37:15] ppdet.engine INFO: Epoch: [3] [200/206] learning_rate: 0.000794 loss: 1.853414 loss_cls: 1.093851 loss_iou: 0.167201 loss_dfl: 0.688709 loss_l1: 0.191645 eta: 2:26:16 batch_cost: 0.5616 data_cost: 0.0003 ips: 14.2438 images/s
[06/01 12:37:21] ppdet.engine INFO: Epoch: [4] [  0/206] learning_rate: 0.000800 loss: 1.853414 loss_cls: 1.082545 loss_iou: 0.167922 loss_dfl: 0.688709 loss_l1: 0.194466 eta: 2:26:43 batch_cost: 0.5756 data_cost: 0.0080 ips: 13.8986 images/s
[06/01 12:38:36] ppdet.engine INFO: Epoch: [4] [100/206] learning_rate: 0.000897 loss: 1.758096 loss_cls: 0.987893 loss_iou: 0.174324 loss_dfl: 0.682745 loss_l1: 0.194987 eta: 2:26:02 batch_cost: 0.5711 data_cost: 0.0003 ips: 14.0068 images/s
[06/01 12:39:50] ppdet.engine INFO: Epoch: [4] [200/206] learning_rate: 0.000994 loss: 1.795157 loss_cls: 0.999800 loss_iou: 0.169698 loss_dfl: 0.678204 loss_l1: 0.195827 eta: 2:25:03 batch_cost: 0.5615 data_cost: 0.0003 ips: 14.2487 images/s
[06/01 12:39:58] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyoloe_plus_crn_m_80e_coco
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
[06/01 12:39:58] ppdet.data.source.coco INFO: Load [414 samples valid, 0 samples invalid] in file /home/aistudio/Car2024/val.json.
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
[06/01 12:39:59] ppdet.engine INFO: Eval iter: 0
[06/01 12:40:19] ppdet.engine INFO: Eval iter: 100
[06/01 12:40:40] ppdet.engine INFO: Eval iter: 200
[06/01 12:40:44] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[06/01 12:40:44] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=1.66s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=4.85s).
Accumulating evaluation results...
DONE (t=1.31s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.572
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.715
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.669
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.642
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.620
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.699
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.737
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.757
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.812
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.729
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700
[06/01 12:40:52] ppdet.engine INFO: Total sample number: 414, average FPS: 9.557503568249556
[06/01 12:40:53] ppdet.engine INFO: Best test bbox ap is 0.572.
[06/01 12:40:54] ppdet.utils.checkpoint INFO: Save checkpoint: output/ppyoloe_plus_crn_m_80e_coco
[06/01 12:40:55] ppdet.engine INFO: Epoch: [5] [  0/206] learning_rate: 0.001000 loss: 1.764578 loss_cls: 0.977293 loss_iou: 0.169698 loss_dfl: 0.676421 loss_l1: 0.194450 eta: 2:25:02 batch_cost: 0.5640 data_cost: 0.0003 ips: 14.1832 images/s
[06/01 12:42:09] ppdet.engine INFO: Epoch: [5] [100/206] learning_rate: 0.001000 loss: 1.738458 loss_cls: 0.985305 loss_iou: 0.167200 loss_dfl: 0.675814 loss_l1: 0.190306 eta: 2:24:17 batch_cost: 0.5716 data_cost: 0.0003 ips: 13.9952 images/s
[06/01 12:43:24] ppdet.engine INFO: Epoch: [5] [200/206] learning_rate: 0.001000 loss: 1.733965 loss_cls: 0.969274 loss_iou: 0.160105 loss_dfl: 0.678193 loss_l1: 0.183968 eta: 2:23:34 batch_cost: 0.5750 data_cost: 0.0003 ips: 13.9119 images/s
[06/01 12:43:31] ppdet.engine INFO: Epoch: [6] [  0/206] learning_rate: 0.001000 loss: 1.729218 loss_cls: 0.963780 loss_iou: 0.158756 loss_dfl: 0.675050 loss_l1: 0.182651 eta: 2:23:51 batch_cost: 0.5912 data_cost: 0.0109 ips: 13.5321 images/s
[06/01 12:44:45] ppdet.engine INFO: Epoch: [6] [100/206] learning_rate: 0.000999 loss: 1.613363 loss_cls: 0.892368 loss_iou: 0.150079 loss_dfl: 0.648608 loss_l1: 0.161639 eta: 2:22:53 batch_cost: 0.5655 data_cost: 0.0003 ips: 14.1480 images/s
[06/01 12:45:59] ppdet.engine INFO: Epoch: [6] [200/206] learning_rate: 0.000999 loss: 1.648051 loss_cls: 0.876875 loss_iou: 0.154434 loss_dfl: 0.659376 loss_l1: 0.170266 eta: 2:21:52 batch_cost: 0.5619 data_cost: 0.0003 ips: 14.2379 images/s
[06/01 12:46:05] ppdet.engine INFO: Epoch: [7] [  0/206] learning_rate: 0.000999 loss: 1.662382 loss_cls: 0.876875 loss_iou: 0.154701 loss_dfl: 0.660583 loss_l1: 0.170266 eta: 2:22:01 batch_cost: 0.5708 data_cost: 0.0086 ips: 14.0163 images/s
[06/01 12:47:19] ppdet.engine INFO: Epoch: [7] [100/206] learning_rate: 0.000998 loss: 1.652610 loss_cls: 0.915990 loss_iou: 0.153202 loss_dfl: 0.654600 loss_l1: 0.164406 eta: 2:20:56 batch_cost: 0.5584 data_cost: 0.0003 ips: 14.3269 images/s
^C

In [ ]

# 当训练完成,如有需要,可以删除所有的checkpoint,只保留best_model
%cd ~/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco
!find . -type f -name '[0-9]*'
!find . -type f -name '[0-9]*' -exec rm -f {} \;
!echo "delete checkpoints complete!"

4.模型导出

出现full_graph问题就打开trainer.py把full_graph=True删掉即可

In [14]

%cd /home/aistudio/PaddleDetection-release-2.6
# 将"-o weights"里的模型路径换成你自己训好的模型
!python tools/export_model.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml \
    -o weights=/home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams \
    TestReader.fuse_normalize=true
/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
[06/01 12:50:19] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[06/01 12:50:19] ppdet.engine INFO: Export inference config file to output_inference/ppyoloe_plus_crn_m_80e_coco/infer_cfg.yml
[06/01 12:50:25] ppdet.engine INFO: Export model and saved in output_inference/ppyoloe_plus_crn_m_80e_coco

5.模型预测

可以运行了看一下对每张图片的识别,比对后可以针对性进行数据增强

In [16]

%cd /home/aistudio/PaddleDetection-release-2.6
# infer_img表示预测改路径的单张图片
# infer_dir表示对该路径下的所有图片进行预测
# draw_threshold表示置信度大于该值的框才画出来
# 生成的图片保存在/home/aistudio/work/PaddleDetection/infer_output
!python tools/infer.py \
    -c configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml \
    --infer_dir=/home/aistudio/Car2024/images \
    --output_dir=infer_output/ \
    --draw_threshold=0.5 \
    -o weights=/home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams
/home/aistudio/PaddleDetection-release-2.6
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized
W0601 12:51:06.215662  5482 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.0, Runtime API Version: 11.2
W0601 12:51:06.221030  5482 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
[06/01 12:51:09] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/PaddleDetection-release-2.6/output/ppyoloe_plus_crn_m_80e_coco/best_model.pdparams
[06/01 12:51:09] train INFO: Found 2069 inference images in total.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
  3%|█▏                                       | 58/2069 [00:13<06:17,  5.32it/s]^C
  3%|█▏                                       | 58/2069 [00:13<07:50,  4.27it/s]
Traceback (most recent call last):
  File "tools/infer.py", line 236, in <module>
    main()
  File "tools/infer.py", line 232, in main
    run(FLAGS, cfg)
  File "tools/infer.py", line 187, in run
    visualize=FLAGS.visualize)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/engine/trainer.py", line 983, in predict
    outs = self.model(data)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/meta_arch.py", line 76, in forward
    outs.append(self.get_pred())
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/yolo.py", line 150, in get_pred
    return self._forward()
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/architectures/yolo.py", line 81, in _forward
    body_feats = self.backbone(self.inputs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 309, in forward
    x = stage(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 221, in forward
    y2 = self.blocks(self.conv2(x))
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/container.py", line 98, in forward
    input = layer(input)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 157, in forward
    y = self.conv1(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/home/aistudio/PaddleDetection-release-2.6/ppdet/modeling/backbones/cspresnet.py", line 61, in forward
    x = self.conv(x)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 930, in __call__
    return self._dygraph_call_func(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py", line 915, in _dygraph_call_func
    outputs = self.forward(*inputs, **kwargs)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/conv.py", line 678, in forward
    use_cudnn=self._use_cudnn)
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/functional/conv.py", line 144, in _conv_nd
    pre_bias = getattr(_C_ops, op_type)(x, weight, *attrs)
KeyboardInterrupt

五、打包代码

1.生成output.txt便于之后尝试跑通predict

In [17]

import os  
# 指定要搜索的文件夹路径  
folder_path = "/home/aistudio/Car2024/images"  
# 指定要写入的txt文件路径  
output_file = "/home/aistudio/work/submission/output.txt"
# 获取文件夹下的所有文件和子文件夹  
files = os.listdir(folder_path)
# 打开txt文件准备写入  
with open(output_file, "w") as file:  
    # 遍历所有文件和子文件夹  
    for item in files:  
        # 如果不是文件夹,则写入文件路径到txt文件中  
        if not os.path.isdir(os.path.join(folder_path, item)):  
            file.write(os.path.join(folder_path, item) + "\n")

2.将导出的模型复制到submission/model中

In [22]

# 在work目录下整理提交代码
# 创建model文件夹
if not os.path.exists('/home/aistudio/work/submission/model/'):
    os.makedirs('/home/aistudio/work/submission/model/') #创建路径

if not os.path.exists('/home/aistudio/work/submission/PaddleDetection/deploy/python'):
    os.makedirs('/home/aistudio/work/submission/PaddleDetection/deploy/python')


# 将检测模型拷贝到model文件夹中
!cp -r /home/aistudio/PaddleDetection-release-2.6/output_inference/ppyoloe_plus_crn_m_80e_coco/* /home/aistudio/work/submission/model/
#!cp -r /home/aistudio/PaddleDetection-release-2.6/deploy/python/* /home/aistudio/work/submission/PaddleDetection/deploy/python
#!cp -r /home/aistudio/PaddleDetection-release-2.6/tools/train.py /home/aistudio/work/submission
# !rm -rf /home/aistudio/work/submission/model/model.pdiparams.info

3.尝试跑通predict

predict能运行成功就能提交成功

In [20]


# 可能耗时较长,40s-100s不等
%cd ~/work/submission
!python predict.py output.txt result.json
/home/aistudio/work/submission
Model Arch: YOLO
--transform op: Resize
--transform op: Permute
total time: 202.27240180969238

4.打包下载

在submission目录下下载submission.zip

In [21]


# 打包代码
%cd /home/aistudio/work/submission/
!zip -r -q -o submission.zip model/ PaddleDetection/ train.py predict.py
/home/aistudio/work/submission
第十八届全国大学生智能车竞赛百度智慧交通组赛题解析与参赛指南 全国大学生智能车竞赛百度智慧交通组已连续举办十八届,成为国内高校AI工程实践的重要舞台。本届赛事以"智慧农场"和"长江之歌"两大场景为核心,深度融合飞桨平台、文心大模型等百度核心技术栈。我曾带队参与过三届赛事评审,发现今年的赛题设计尤其强调。我曾见过一支队伍在最后48小时重构了整个控制算法,最终逆袭夺冠。他们的经验是:保持模块化设计,确保每个组件都可独立替换。记住,智能车竞赛不仅是技术比拼,更是工程管理能力的试金石。注意:实际开发中建议使用Paddle-Lite的NPU加速功能,可减少30%内存占用。 阅读详情

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在面对这场没有硝烟的战争时,除了医护人员在前线与病毒搏斗,还有一群默默的英雄——数据科学家和分析师,他们利用手中的“数据武器”,昼夜不停地监测、分析、预测疫情走势,为政府决策、资源调配、公众科普提供了至关重要的支撑。同时,由于某些国家(如美国、加拿大、澳大利亚、中国等)的数据细化到省/州级别,为了得到国家层面的总数据,我们需要按国家和日期进行聚合。正是在这样的背景下,经过这些步骤,我们已经得到了一个包含了累计数据、每日新增数据和活跃病例数的完整数据集,可以开始进行深入的分析和可视化了!

wylee的博客 1165

第十九届全国大学生智能汽车竞赛-竞速比赛模型组比赛细则

车模通过危险区、救援区、坡道、追逐区四个特殊路段时冲出赛道无法在该区域驶回赛道或停在赛道上无法继续前进时,视为通过失败,此时发车手可手动救援,重新将车辆置于该特殊路段靠后位置启动,每次出发仅可使用一次手动救援,救援后额外罚时。本届完全模型组场地为“城市反恐”主题,模拟特种智能车辆在城市执行巡逻任务,车辆不但需要自主驶过常规道路,还要根据沿途的任务元素所代表的突发事件,驶入特殊区域,并按指定方式通过。为了引导比赛任务的完成,在比赛赛道的任务元素和特殊元素区域的前方路旁的指定区域放有三维模型实体标志。

TSINGHUAJOKING 4万+

Unity透明视频播放全攻略:AVPro Video插件实战与避坑指南

在游戏开发与交互式内容创作中,视频播放是常见的多媒体需求。其核心原理在于通过解码器将压缩的视频数据流转换为连续的图像帧,再经由图形API渲染至屏幕。这项技术的价值在于能够高效地呈现动态视觉内容,广泛应用于游戏过场动画、UI特效、AR/VR虚拟展示等场景。当需求进阶到需要背景透明的视频时,技术实现复杂度显著增加,这涉及到对视频Alpha通道的编码、解码与渲染管线的特殊处理。本文聚焦于Unity引擎中实现高质量透明视频播放的工程实践,深入解析了AVPro Video这一专业插件如何解决原生VideoPlayer

weixin_30368981的博客 384

[cryoEM] cryoSPARC简介

cryoSPARC系统结构概述 cryoSPARC是一个后端和前端高性能计算软件系统,为单颗粒冷冻电镜提供数据处理和图像分析能力,以及丰富的基于浏览器的用户截面和命令行工具。 Master-worker模式 系统基于主用户模式。 主进程(web应用,核心应用程序和MongoDB数据库)一起在主节点上运行。主节点需要相对轻量级的资源(2+ CPU< 16GB+ RAM)。 工作程序进程可以在具有GPU的任何可用节点上生成,负责所有实际计算和数据处理,并由主节点调度。注意:同一节点可以同时充当主节点和辅

进阶中的程序员吃吃的博客 7798

浅谈第十八届智能车完全模型组

十八届智能车完全模型组

CGONE123的博客 3512

windows使用jupyter notebook读取文件路径的几种方法和设置默认打开路径

简单分享小虎这段时间以来学到的jupyter notebook操作技巧。

狂小虎/Anthony Dave的博客 KuangXiaoHu's Blogs 1万+

【PP-YOLOE+】第18届全国大学生智能汽车竞赛百度完全模型组线上资格

全国大学生智能汽车竞赛是以智能汽车为研究对象的创意性科技竞赛,是面向全国大学生的一种具有探索性工程的实践活动,是教育部倡导的大学生A类科技竞赛之一。竞赛以立足培养,重在参与,鼓励探索,追求卓越为指导思想,培养大学生的创意性科技竞赛能力。本次比赛数据由赛曙科技(SASU)提供,模型和基线相关技术支持由深度学习技术平台部提供,一站式AI开发平台AI Studio由百度AI技术生态部提供。要求在统一的计算资源下,能够对农业相关物体的具体位置和类别进行快速精准的识别。

m0_63642362的博客 3331

优快云独家首发!万字长文,YOLOv5/v7/v8算法模型yaml文件史上最详细解析与教程!小白也能看懂!掌握了这个就掌握了魔改YOLO的核心!

🍀🍀🍀YOLOv5/v7/v8算法模型yaml文件史上最详细解析与教程,全网最强,学不会算我输!!!

Coding... 1万+

零基础入门转录组数据可视化——绘制差异小提琴图(常规)

转录组数据可视化之一,先介绍相关基础知识,其次用实际案例为基础,由浅入深介绍差异小提琴图绘制的全过程,包括内容有相关代码和截屏。

呆猪儿的博客 637

智能车比赛人工智能模型组(难点)

使用ppyoloe+_m训练模型时候,默认训练的是coco2017数据集,需要自己修改配置。

2501_92014752的博客 53

Ubuntu 部署 Ollama 避坑指南:下载失败 / 服务崩溃?一招解决所有问题!

本文详细介绍了在Ubuntu系统上安装Ollama大模型工具的完整流程和常见问题解决方案。主要内容包括:环境检查(系统版本、架构、依赖)、一键安装步骤、端口配置和服务启动等核心操作。重点针对下载失败、服务启动异常、权限不足等高频问题,提供了国内镜像配置、手动创建服务文件、用户组添加等实用解决方案。最后通过测试Qwen2.5轻量模型验证安装效果,并给出低配设备选型建议。全文提供可复制的命令行操作,帮助用户快速搭建离线可用的大模型环境。

2501_93651177的博客 1008

第二十届全国大学生智能汽车竞赛 竞速比赛完全模型组规则(初稿)

本届完全模型组场地为“智慧生活”主题,模拟无人驾驶车辆搭载乘客在城镇中完成载客任务,行驶周期内,车辆不但需要自主驶过常规道路和坡道桥梁,依照安全原则躲避路上的行人、锥桶和路障,还要识别沿途的特殊元素所代表的任务内容,如汽车餐厅取餐、充电站充电、临时停车接送乘客等,并按指定方式通过。小车来到餐饮区的汽车餐厅,如图所示由路旁快餐店标志指示,在该区域需要行驶进入标志对面的斜45度的路线上,停车片刻进行取餐,未驶入停车区域罚时5秒,驶入取餐区但无明显停车动作罚时2秒。不停车罚时5秒,未完全停在区域内罚时2秒。

TSINGHUAJOKING 2万+

汉书HE8,好用的汇编代码编写工具

汉书HE8,好用的汇编代码编写工具早期编写汇编代码所用功能强大

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

安全帽佩戴检测算法模型训练详细流程

paddle实现安全帽佩戴检测算法

Trisyp的博客 1480

jre9下载 jdk9

(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】

第十七届全国大学生智能车竞赛完全模型组 I 型车模数据

TSINGHUAJOKING 6583

数学建模模型总结

这是本人在两年内准备参加数学建模竞赛搜集到的模型资料,已经经过了本人的筛选和整理,希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助!

第十七届全国大学生智能车竞赛完全模型组比赛细则-最终方案

  §01 智能车竞赛 背景介绍   第十七届 全国大学生智能车竞赛 将于2022年暑期举行, 竞速比赛规则 已于2021年11月公布。竞速比赛分为九个赛题组,其中"智慧视觉组"、"完全模型组"两个组别具有更加复杂的机器视觉处理和模型 车运动控制任务,比赛详细内容将会另行通过比赛细则进行说明。   本文是完全模型组比赛细则。比赛细则正式文档将会公布在全国大学生智能车竞赛网站。 smartcar.cdstm.cn ▲ 图1.1.1 全国大学生智能车竞赛网站 完全模型组描述   在智能车竞赛.

TSINGHUAJOKING 1万+

2024年国赛来啦!!第十九届全国大学生智能汽车竞赛完全模型组启动说明会

为赛事定制的I型车模,外观DIY自由度极高,搭载拥有澎湃边缘算力的百度EdgeBoard计算卡,提供充足AI算力,同学们将有足够空间做出颜值与实力并存的个性车模!”,模拟特种智能车辆在城市执行巡逻任务,车辆不但需要自主驶过常规道路,还要根据沿途的任务元素所代表的突发事件,驶入特殊区域,并按指定方式通过。我们有丰富完备的培训计划、资料体系和答疑机制,为同学们一路冲关保驾护航。”交互,道具车身有AI待识别标志,选手需要根据图案,来判断该道具车的身份,去实现车与车之间的超越、逼停、推出等动作,在赛道中上演精彩“

TSINGHUAJOKING 1万+

名单公示|2025全国大学生智能汽车竞赛—— 百度完全模型组AI板卡借用团队公布!

比赛每年吸引了包括清华、上交、复旦、北航等500多所理工类高校参加,在全国数百所高校的支持下,全国大学生智能汽车竞赛至今已成功举办了十九届,参赛学生总规模超过 35万人次,学生收获和竞赛声誉保持高位。完全模型组2025赛道主题为“智慧生活”,模拟无人驾驶车辆搭载乘客在城镇中完成载客任务,行驶周期内,车辆不但需要自主驶过常规道路和坡道桥梁,依照安全原则躲避路上的行人、锥桶和路障,还要识别沿途的特殊元素所代表的任务内容,如汽车餐厅取餐、充电站充电、临时停车接送乘客等,并按指定方式通过。

TSINGHUAJOKING 2581

2024年全国大学生智能汽车竞赛完全模型组线上技术培训来了!你准备好了吗?

为了让大家更好地掌握智能汽车完全模型组软硬件构架和调试技巧,百度联合赛曙科技将于本周六(3月9日))17:00在B站直播间(飞桨PaddlePaddle)组织线上技术培训,欢迎各位同学积极参与本次线上培训,不断提升自己的技能水平,为今年的备赛打下坚实的基础。扫码加入官方QQ交流群,获取更多赛项咨询、技术培训和学习资料。截至2023年,已经举办十八届,比赛每年吸引包括清华、上交、复旦、北航等500多所高校,超10万名大学生参加,是提升学生创新实践能力和培养团队精神的国家级创意性科技竞赛。

TSINGHUAJOKING 1141

手把手教你在百度飞桨云平台下运行PPYOLO-E,训练COCO数据集

手把手教你在百度飞桨平台使用PPYOLOE训练COCO数据集。

qq_44992785的博客 7980

[Paddle Detection]基于PP-YOLOE+实现道路场景目标检测及部署

该项目着眼于基于视觉深度学习的自动驾驶场景,旨在对车载摄像头采集的视频数据进行道路场景解析,为自动驾驶提供一种解决思路。利用YOLO系列模型PP_YOLOE+完成车辆检测实现一种高效高精度的道路场景解析方式,从而实现真正意义上的自动驾驶,减少交通事故的发生,保障车主的人身安全。数据集地址视频数据: 超过1,100小时的100000个高清视频序列在一天中许多不同的时间,天气条件,和驾驶场景驾驶经验。视频序列还包括GPS位置、IMU数据和时间戳。道路目标检测。

ai领域探索者,欢迎一起交流,目前从事具身智能行业。 4806

智能车竞赛技术报告 | 智能车视觉 - 中南大学 - 中南大学比亚迪午马2021

简 介: 本文设计的智能车系统以RT1064微控制器为核心控制单元,通过车体上方的灰度摄像头检测赛道信息,采用了光电编码器检测模型车的实时速度,使用PID 控制算法调节驱动左右电机的转速,实现了对车运动速度和运动方向的闭环控制。使用OpenArt Mini摄像头实现智能识别。为了提高模型车的速度和稳定性,使用QT上位机、键盘模块等调试工具,进行了大量硬件与软件测试。实验结果表明,该系统设计方案确实可行。 学 校:中南大学      队伍名称:中南大学比亚迪午马2021参赛队员:龚元昌      ..

TSINGHUAJOKING 7158

全模型组的测试初步结果

简 介: 完全模型组 是 第十七届全国大学智能汽车竞赛竞速比赛 中依据百度PaddlePaddle人工智能平台,将人工智能技术向智能车竞赛竞速比赛自认延伸的一个组别。该组别采用由百度提供的 III车模,在百度的EdgeBoard与Infineon单片机控制下,在正常的赛道上,完全有人工智能视觉模型支持车模在赛道上以及赛道下完成相应的运行动作。等待初步测试结果形成之后,将会公布完全模型比赛细则。 关键词: 完全模型组,智能车竞赛,百度 #mermaid-svg-bog17yG0XORewaG0 .lab..

TSINGHUAJOKING 5728

第二十届全国大学生智能汽车竞赛-竞速比赛 百度完全模型组规则V1.0

车壳必须完整的包裹车辆的本身,车身的底板外边缘,4 个轮子和越过车壳的传感器及其支撑件外,车辆的正视图,侧视图和俯视图看不到车辆的内部细节。标牌摆放时与赛道方向垂直,有图案的一面朝向小车驶来的方向,摆放偏差不超过±10度。本届完全模型组场地为“智慧生活”主题,模拟无人驾驶车辆搭载乘客在城镇中完成载客任务,行驶周期内,车辆不但需要自主驶过常规道路和坡道桥梁,依照安全原则躲避路上的行人、锥桶和路障,还要识别沿途的特殊元素所代表的任务内容,如汽车餐厅取餐、充电站充电、临时停车接送乘客等,并按指定方式通过。

TSINGHUAJOKING 2万+

智能车竞赛完全模型组规则解析与实战准备

车模需完成载客、避障、取餐等复合任务:餐饮区需45度斜向停车,临时停车点要求四轮完全进入虚线区域,充电站需识别蓝色车模避免碰撞。每个环节都有对应的罚时机制,例如碰撞行人罚时10秒,这对路径规划算法提出更高要求。上快速搭建仿真环境时,我发现其预置的百度Paddle框架和3D场景组件能大幅降低开发门槛。通过可视化拖拽即可构建符合官方标准的赛道模型,实时渲染功能让算法调试效率提升明显,特别适合赛前进行多场景验证。官方提供的STL文件建议用"三和"牌喷漆上色,这是影响识别精度的关键细节。

StarfallOwl89的博客 455
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