removed old models, updated README

README.md

@@ -1,58 +1,88 @@
 # Semantic Segmentation
 
-🚧 This is still under development 🏗
+🚧 This project still under development. Some scripts might be incomplete. 🏗
 
 Semantic segmentation has become an important component of self-driving vehicles. It allows the car to understand the surroundings by classifying every pixel of the input image.
 
 ![](./media/image-1.png)
 
-## Running...
+## 🏃🏻‍♂️ Running...
 To run inference on the pre-trained models, please use `segmentor.py`. 
 
 	from segmentor import Segmentor
 	seg = Segmentor()
-	cls_output, img_viz = seg.semantic_segmentation(image=image, visualization=True)
+	classes_output, img_viz = seg.semantic_segmentation(image=image, visualization=True)
 
-`cls_output` is the pixel-wise classification result for all the categories. `img_viz` is a RGB image generated based on `cls_output`
+`classes_output` is the pixel-wise classification result for all the categories. `img_viz` is a RGB image generated based on `classes_output `
 
 The best way to run some actual test is using `test.py`. You must specify the image path by changing the `path` variable. 
 
 The pre-trained weights are stored in the `./weights` directory.
 
+## The Cityscape Dataset 💼
+
+In order to train the model, please download the cityscape dataset, which can be found [here](https://www.cityscapes-dataset.com/). 
+
+Remeber to preprocess the data using this jupyter notebook: `Data Preprocessing.ipynb`. The script will generate `train_labels.csv` and `val_labels.csv`
+
+My data is organized as such:
+
+```
+Cityscape
+│   train_labels.csv
+│   val_labels.csv 
+└─── training
+│   └─── aachen
+│   └─── augsburg
+│   .
+│ 	.
+└─── training_gt
+│   └─── aachen
+|   └─── augsburg
+|	.
+|	.
+└─── val
+│   └─── frankfurt
+│   └─── lindau
+└─── val_gt
+|   └─── frankfurt
+|   └─── lindau
+```
+
+![](./media/screenshot.png)
+
 ## Training
 
-`train.py` is the training script. `utils.py` contains all the categories (classes). You can modify them based on your dataset. 
+There are two training scripts:
+
+- `train_icnet.py`
+- `train_fusion.py`
 
-*training doesn't work very well.* I trying to fix the issue. 
+`train.py` is the ICNet training script. `utils.py` contains all the categories (classes). You can modify them based on your dataset. 
 
 ## Models
 
-The goal is to achieve real time semantic segmentation. Therefore, choosing & developing the appropriate model is critical. 
-I experimented with UNet and ENet, but realized drawbacks to both of these architectures. I eventually landed on ICNet, which
-stands for image cascade network
+An overview of the different segmentation models in this project. 
+
+### ICNet
+
+ICNet (or image cascade network) is a realtime semantic segmentation model developed by Zhao et al. at The Chinese University of Hong Kong. Their paper shows that ICNet can achieve mIoU of ~70% with the Cityscape dataset, while running at ~30 FPS. After some testing, ICNet became a great choice for self-driving applications. (I am currently using the network on my [self-driving golf cart project](https://github.com/sigmaai/self-driving-golf-cart))
 
 ![](./media/model_overview.png)
 
 Here is a simple benchmark comparison between ICNet and other popular semantic segmentation models. These images and visualizations
 are from the original ICNet paper, which can be found [here](https://arxiv.org/abs/1704.08545).  
 
 <center>
-<img src="./media/model_comparison.png" alt="image" width="500">
+<img src="./media/model_comparison.png" alt="image" width="350">
 </center>
 
-## About
+### FusionNet 🤔
 
-This project is created for the self-driving golf cart project that I have been working on. For more information on that, please refer to the [Github page](https://github.com/xmeng17/self-driving-golf-cart), or my [website](https://neilnie.com/the-self-driving-golf-cart-project/).
+Coming soon...
 
-If you have questions, comments or concerns, please contact me at [contact@neilnie.com](mailto:contact@neilnie.com). 
-
-## Others
-
-#### Details about trained models
-`./weights/new-enet-5.h5`: 512x512x26
-
-`./weights/enet-c-v1-2.h5`: 640x320x16
+## About
 
-`./weights/enet-c-v2-1.h5`: 1024x512x16
+This project is created for the self-driving golf cart project that I have been working on. For more information on that, please refer to the [Github page](https://github.com/xmeng17/self-driving-golf-cart), or my [website](https://neilnie.com/the-self-driving-golf-cart-project/).
 
-The channel value correspond to the number of classes.
+If you have questions, comments or concerns, please contact me at [contact@neilnie.com](mailto:contact@neilnie.com).