Update README.md

README.md

@@ -7,22 +7,20 @@ license:
 ---
 
 ## Multiclass semantic segmentation using DeepLabV3+
-
 This repo contains the model and the notebook [to this Keras example on Multiclass semantic segmentation using DeepLabV3+](https://keras.io/examples/vision/deeplabv3_plus/).
 
 Full credits to: [Soumik Rakshit](http://github.com/soumik12345)
 
 ## Background Information 
-Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.
-
-References
-[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)
-[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)
-[DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)
+Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.   
 
 ## Training Data
-
 The model is trained on a subset (10,000 images) of [Crowd Instance-level Human Parsing Dataset](https://arxiv.org/abs/1811.12596). The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.
 
 ## Model
-The model uses ResNet50 pretrained on ImageNet as the backbone model.
+The model uses ResNet50 pretrained on ImageNet as the backbone model.
+
+References:   
+[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)   
+[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)   
+[DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)