登录
首页 » Python » 完成版LaneNet

完成版LaneNet

于 2020-10-28 发布
0 256
下载积分: 1 下载次数: 2

代码说明:

说明:  基于SegNet实现了车道线的识别。里面包含已经训练好的模型。(Lane line recognition based on SegNet contains the trained model.)

文件列表:

data, 0 , 2018-12-30
data\source_image, 0 , 2018-12-30
data\source_image\accuracy.png, 48361 , 2018-12-13
data\source_image\binary_seg_loss.png, 47406 , 2018-12-13
data\source_image\instance_seg_loss.png, 45704 , 2018-12-13
data\source_image\lanenet_batch_test.gif, 40673826 , 2018-12-13
data\source_image\lanenet_binary_seg.png, 51954 , 2018-12-13
data\source_image\lanenet_embedding.png, 643503 , 2018-12-13
data\source_image\lanenet_instance_seg.png, 37788 , 2018-12-13
data\source_image\lanenet_mask_result.png, 1007811 , 2018-12-13
data\source_image\network_architecture.png, 178176 , 2018-12-13
data\source_image\total_loss.png, 43865 , 2018-12-13
data\training_data_example, 0 , 2018-12-30
data\training_data_example\gt_image_binary, 0 , 2018-12-30
data\training_data_example\gt_image_binary\0000.png, 6807 , 2018-12-13
data\training_data_example\gt_image_binary\0001.png, 6849 , 2018-12-13
data\training_data_example\gt_image_binary\0002.png, 7700 , 2018-12-13
data\training_data_example\gt_image_binary\0003.png, 7293 , 2018-12-13
data\training_data_example\gt_image_binary\0004.png, 6584 , 2018-12-13
data\training_data_example\gt_image_binary\0005.png, 6632 , 2018-12-13
data\training_data_example\gt_image_instance, 0 , 2018-12-30
data\training_data_example\gt_image_instance\0000.png, 7598 , 2018-12-13
data\training_data_example\gt_image_instance\0001.png, 7652 , 2018-12-13
data\training_data_example\gt_image_instance\0002.png, 8654 , 2018-12-13
data\training_data_example\gt_image_instance\0003.png, 8226 , 2018-12-13
data\training_data_example\gt_image_instance\0004.png, 7313 , 2018-12-13
data\training_data_example\gt_image_instance\0005.png, 7370 , 2018-12-13
data\training_data_example\image, 0 , 2018-12-30
data\training_data_example\image\0000.png, 1113990 , 2018-12-13
data\training_data_example\image\0001.png, 1135520 , 2018-12-13
data\training_data_example\image\0002.png, 1210780 , 2018-12-13
data\training_data_example\image\0003.png, 1192757 , 2018-12-13
data\training_data_example\image\0004.png, 1166130 , 2018-12-13
data\training_data_example\image\0005.png, 1085884 , 2018-12-13
data\training_data_example\train.txt, 988 , 2018-12-13
data\training_data_example\val.txt, 493 , 2018-12-13
data\tusimple_test_image, 0 , 2018-12-30
data\tusimple_test_image\0.jpg, 183035 , 2018-12-13
data\tusimple_test_image\1.jpg, 213446 , 2018-12-13
data\tusimple_test_image\2.jpg, 189109 , 2018-12-13
data\tusimple_test_image\3.jpg, 221499 , 2018-12-13
data\tusimple_test_image\4.jpg, 211132 , 2018-12-13
data\tusimple_test_image\ret, 0 , 2018-12-30
data\tusimple_test_image\ret\0.jpg, 204076 , 2018-12-29
data\tusimple_test_image\ret\1.jpg, 226300 , 2018-12-29
data\tusimple_test_image\ret\2.jpg, 205588 , 2018-12-29
data\tusimple_test_image\ret\3.jpg, 234343 , 2018-12-29
data\tusimple_test_image\ret\4.jpg, 222604 , 2018-12-29
tools, 0 , 2019-03-30
tools\__pycache__, 0 , 2018-12-30
tools\__pycache__\cnn_basenet.cpython-35.pyc, 14265 , 2018-12-29
tools\__pycache__\dense_encoder.cpython-35.pyc, 6066 , 2018-12-29
tools\__pycache__\fcn_decoder.cpython-35.pyc, 2872 , 2018-12-29
tools\__pycache__\global_config.cpython-35.pyc, 879 , 2018-12-29
tools\__pycache__\lanenet_cluster.cpython-35.pyc, 6235 , 2018-12-29
tools\__pycache__\lanenet_discriminative_loss.cpython-35.pyc, 3924 , 2018-12-29
tools\__pycache__\lanenet_merge_model.cpython-35.pyc, 4976 , 2018-12-29
tools\__pycache__\lanenet_postprocess.cpython-35.pyc, 2620 , 2018-12-29
tools\__pycache__\vgg_encoder.cpython-35.pyc, 4484 , 2018-12-29
tools\cnn_basenet.py, 16846 , 2018-12-13
tools\dense_encoder.py, 7947 , 2018-12-29
tools\fcn_decoder.py, 3425 , 2018-12-29
tools\generate_tusimple_dataset.py, 6337 , 2018-12-13
tools\global_config.py, 1643 , 2018-12-13
tools\lanenet_cluster.py, 6823 , 2018-12-13
tools\lanenet_discriminative_loss.py, 5494 , 2018-12-13
tools\lanenet_merge_model.py, 7253 , 2018-12-29
tools\lanenet_postprocess.py, 2565 , 2018-12-13
tools\test_lanenet.py, 9905 , 2019-03-30
tools\train_lanenet.py, 14860 , 2018-12-13
tools\vgg_encoder.py, 6720 , 2018-12-29

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • voronoi
    能够得到二维、三维voronoi图,修改后可以获得任意数量的voronoi图。(The two or thress-dimensional Voronoi diagram can be obtained, and any number of Voronoi diagrams can be obtained after modification.)
    2020-12-21 08:59:08下载
    积分:1
  • ShowWMF
    显示WMF格式位图!(show WMF format bitmaps!)
    2005-01-29 09:00:52下载
    积分:1
  • Arnold
    具有可逆性与周期性的Arnold置乱变换,目的是实现对图像的置乱,用于图像加密中(The realization of image scrambling, used in image encryption)
    2017-12-07 21:48:32下载
    积分:1
  • threshold
    python语言,三种阈值分割方式,简单阈值分割,自适应阈值分割,OTsu分割方式(Python language, three methods of image segmentation , simple threshold segmentation, adaptive threshold segmentation, OTsu segmentation)
    2019-04-06 16:08:23下载
    积分:1
  • gui界面
    说明:  利用matlab的gui设计的带有输入和输出功能的界面图形,可视性很强,学习非常值得(The interface graphics with input and output functions designed by GUI of MATLAB have strong visibility and worth learning.)
    2020-06-19 18:20:01下载
    积分:1
  • qpegps_0.2.3.tar
    用于PDA的GPS源代码(for PDA GPS source code)
    2004-11-13 15:09:56下载
    积分:1
  • fdkReconstruction
    fdk图像重建,需要C++和MATLAB联合使用进行调式(FKD image construction)
    2017-11-27 12:39:37下载
    积分:1
  • jiancesuanfa
    说明:  边缘检测算法 速度快 你一定会满意的。以前是要收费的。现在免费给你们。 源码经过测试真确无误。 (edge detection algorithm faster you will surely satisfied. Prior to the charges. Now free to you. Source tested truthful.)
    2021-03-31 00:09:09下载
    积分:1
  • 如何获取屏幕上各颜色的红、绿、蓝值
    如何获取屏幕上各颜色的红、绿、蓝值(how to access the colors on the screen in red, green and blue values)
    2004-11-22 15:31:50下载
    积分:1
  • facet边缘检测的matlab源代码
    说明:  facet边缘检测的matlab源代码,是基于二阶模型,只要运行最后一个hhh.m就可以了(facet Edge Detection Matlab source code is based on second-order model, as long as the last one run on the hhh.m)
    2005-11-04 16:01:14下载
    积分:1
  • 696516资源总数
  • 106658会员总数
  • 16今日下载