modeified readme.md

README.md

@@ -1 +1,94 @@
-# lanenet-lane-detection
+# LaneNet-Lane-Detection
+Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the paper "Towards 
+End-to-End Lane Detection: an Instance Segmentation Approach".You can 
+refer to their paper for details https://arxiv.org/abs/1802.05591. 
+This model consists of a encoder-decoder stage, binary semantic segmentation stage and instance semantic segmentation 
+using discriminative loss function for real time lane detection task.
+
+## Installation
+This software has only been tested on ubuntu 16.04(x64), python3.5, cuda-8.0, cudnn-6.0 with a GTX-1070 GPU. 
+To install this software you need tensorflow 1.3.0 and other version of tensorflow has not been tested but I think 
+it will be able to work properly in tensorflow above version 1.0. Other required package you may install them by
+
+```
+pip3 install -r requirements.txt
+```
+
+## Test model
+In this repo I uploaded a model trained on tusimple lane dataset[Tusimple_Lane_Detection](http://benchmark.tusimple.ai/#/). 
+You can test a single image on the trained model as follows
+
+```
+python tools/test_lanenet.py --is_batch False --batch_size 1 
+--weights_path model/tusimple_lanenet/tusimple_lanenet_vgg_2018-05-21-11-11-03.ckpt-94000 
+--image_path data/tusimple_test_image/0.jpg
+```
+The results are as follows:
+
+`Test Input Image`
+![Test Input](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/test_output.png)
+`Test Lane Mask Image`
+![Test Lane_Mask](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/test_output.png)
+`Test Lane Binary Segmentation Image`
+![Test Lane_Binary_Seg](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/test_output.png)
+`Test Lane Instance Segmentation Image`
+![Test Lane_Instance_Seg](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/test_output.png)
+`Test Lane Instance Embedding Image`
+![Test Lane_Embedding](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/test_output.png)
+
+If you want to test the model on a whole dataset you may call
+```
+python tools/test_lanenet.py --is_batch True --batch_size 2 --save_dir data/tusimple_test_image/ret 
+--weights_path model/tusimple_lanenet/tusimple_lanenet_vgg_2018-05-21-11-11-03.ckpt-94000 
+--image_path data/tusimple_test_image/
+```
+If you set the save_dir argument the result will be saved in that folder or the result will not be saved but be 
+displayed during the inference process holding on 3 seconds per image. I test the model on the whole tusimple lane 
+detection dataset and make it a video. You may catch a glimpse of it bellow.
+`Tusimple test dataset video`
+![tusimple_test_video]()
+
+## Train your own model
+#### Data Preparation
+Firstly you need to organize your training data refer to the data/training_data_example folder structure. And you need 
+to generate a train.txt and a val.txt to record the data used for training the model. 
+
+The training samples are consist of three components. A binary segmentation label file and a instance segmentation label
+file and the original image. The binary segmentation use 255 to represent the lane field and o for the rest. The 
+instance use different pixel value to represent different lane field and 0 for the rest.
+
+All your training image will be scaled into the same scale according to the config file.
+
+#### Train model
+The whole training epoches are 200010 in my experiment. I trained the model with a batch size 4, initialized learning 
+rate is 0.0001 and decrease by multiply 0.96 every 5000 epochs. About training parameters you can check the 
+global_configuration/config.py for details. You can switch --net argument to change the base encoder stage. If you 
+choose --net vgg then the vgg16 will be used as the base encoder stage and a pretrained parameters will be loaded and if
+you choose --net dense then the dense net will be used as the base encoder stage instead and no pretrained parameters 
+will be loaded. And you can modified the training script to load your own pretrained parameters or you can implement 
+your own base encoder stage. To train your own model by
+
+```
+python tools/train_lanenet.py --net vgg --dataset_dir data/training_data_example/
+```
+You can also continue the training process from the snapshot by
+```
+python tools/train_lanenet.py --net vgg --dataset_dir data/training_data_example/ --weights_path path/to/your/last/checkpoint
+```
+
+You may monitor the training process using tensorboard tools
+
+During my experiment the `Total loss` drops as follows  
+![Training loss](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/train_loss.png)
+The `Binary Segmentation loss` drops as follows  
+![Training binary_seg_loss](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/seq_distance.png)
+The `Instance Segmentation loss` drops as follows  
+![Training instance_seg_loss](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/seq_distance.png)
+
+## Experiment
+The accuracy during training process rises as follows  
+![Training accuracy](https://github.com/TJCVRS/CRNN_Tensorflow/blob/master/data/images/training_accuracy.md)
+
+## TODO
+- [ ] Add a embedding visualization tools to visualize the embedding feature map
+- [ ] Training the model on different dataset