Update README.md

README.md

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 KittiSeg trains an FCN based model for Segmentation of Roads on the Kitti road detection dataset. The model achieved [first place](http://www.cvlibs.net/datasets/kitti/eval_road_detail.php?result=ca96b8137feb7a636f3d774c408b1243d8a6e0df) on the Benchmark at submission time and is descripted in our paper: [MultiNet](https://arxiv.org/abs/1612.07695).
 
-![](https://i.imgur.com/Usi1XhE.png)
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 The model is optimized to perform well even with very few data samples avaible. The implementation is trained on just *250* images and receives a state-of-the art MaxF1 score of over *96%*. It's inference time is less then *95ms*.
 
 
 
+![](https://i.imgur.com/Usi1XhE.png)
 
 The code contains for `train`, `evaluate` and `visualize` semantic segmentation in tensorflow. It is build to be compatible with the [TensorVision](http://tensorvision.readthedocs.io/en/master/user/tutorial.html#workflow) backend which allows to organize experiments in a very clean way.
 
@@ -44,9 +37,9 @@ I strongly recommand to exececute step 4. instead of downloading the data yourse
 
 ### Getting started
 
-Run: `python evaluate.py` to evaluate a trained model. This will also download the Kitti Data and model weights, if nessasary.
+Run: `python evaluate.py` to evaluate a trained model. 
 
-Run: `python train.py` to train a new model on the Kitti Data. This will also download the Kitti Data if nessasary.
+Run: `python train.py` to train a new model on the Kitti Data.
 
 ### Modifying Model & Train on your own data
 
@@ -76,9 +69,7 @@ Those modules operate independently. This allows easy experiments with different
 By default, the data is stored in the folder `KittiSeg/DATA` and the output of runs in `KittiSeg/RUNS`. This behaviour can be changed by adjusting the environoment Variabels: `$TV_DIR_DATA` and `$TV_DIR_RUNS`.
 
 For organizing your experiments you can use:
-`python train.py --project batch_size_bench --name size_5`
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-This will store the run in the subfolder:  `$TV_DIR_RUNS/batch_size_bench/size_5_%DATE`
+`python train.py --project batch_size_bench --name size_5`. This will store the run in the subfolder:  `$TV_DIR_RUNS/batch_size_bench/size_5_%DATE`
 
 This is useful if you want to run different series of experiments.
 
@@ -90,31 +81,31 @@ KittiSeg is build on top of the TensorVision [TensorVision](https://github.com/T
 
 To utilize the entire TensorVision functionality install it using 
 
-`$ cd KittiSeg/submodules/TensorVision`
+`$ cd KittiSeg/submodules/TensorVision` <br>
 `$ python setup install`
 
 Now you can use the TensorVision command line tools, which includes:
 
-`tv-train --hypes hypes/KittiSeg.json` trains a json model.
-`tv-continue --logdir PATH/TO/RUNDIR` continues     interrupted training
-`tv-analyze --logdir PATH/TO/RUNDIR` evaluated trained model
+`tv-train --hypes hypes/KittiSeg.json` trains a json model. <br>
+`tv-continue --logdir PATH/TO/RUNDIR` continues interrupted training <br>
+`tv-analyze --logdir PATH/TO/RUNDIR` evaluated trained model <br>
 
 
 ## Useful Flags & Variabels
 
 Here are some Flags which will be useful when working with KittiSeg and TensorVision. All flags are avaible across all scripts. 
 
-`--hypes` : specify which hype-file to use
-`--logdir` : specify which logdir to use
-`--gpus` : specify on which GPUs to run the code 
-`--name` : assign a name to the run
-`--project` : assign a project to the run
-`--nosave` : debug run, logdir will be set to `debug`
+`--hypes` : specify which hype-file to use <br>
+`--logdir` : specify which logdir to use <br>
+`--gpus` : specify on which GPUs to run the code <br>
+`--name` : assign a name to the run <br>
+`--project` : assign a project to the run <br>
+`--nosave` : debug run, logdir will be set to `debug` <br>
 
 In addition the following TensorVision environoment Variables will be useful:
 
-`$TV_DIR_DATA`: specify meta directory for data
-`$TV_DIR_RUNS`: specify meta directiry for output
-`$TV_USE_GPUS`: specify default GPU behavour. 
+`$TV_DIR_DATA`: specify meta directory for data <br>
+`$TV_DIR_RUNS`: specify meta directiry for output <br>
+`$TV_USE_GPUS`: specify default GPU behavour. <br>
 
 On a cluster it is useful to set `$TV_USE_GPUS=force`. This will make the flag `--gpus` manditory and ensure, that run will be executed on the right gpu.