Merging README.MD

README.MD

@@ -1,15 +1,59 @@
 
-# Realtime instance segmentation in the browser
 
 
+# Realtime Semantic Segmentation in the browser
 
-This project uses TensorFlow.js to perform realtime instance segmentation in the browser using [RefineNet](https://arxiv.org/pdf/1611.06612.pdf). Many thanks to [Vladimir Nekrasov](https://drsleep.github.io/) that provided the *.js weights* of the [Light-Weight RefineNet](http://bmvc2018.org/contents/papers/0494.pdf) - a more compact implementation of RefineNet, suitable for tasks requiring real-time performance.
+  
 
+This project uses TensorFlow.js to perform realtime instance segmentation in the browser using [RefineNet](https://arxiv.org/pdf/1611.06612.pdf). Many thanks to  [Bernardo de Oliveira](https://github.com/bernieOllie), that help me to fix some bugs in the code, and [Vladimir Nekrasov](https://drsleep.github.io/) that provided the *.js weights* of the [Light-Weight RefineNet](http://bmvc2018.org/contents/papers/0494.pdf) - a more compact implementation of RefineNet, suitable for tasks requiring real-time performance.
 
-  ![enter image description here](./git_media/demo.gif)
-
+
+  ![enter image description here](./git_media/objects_demo.gif)
+
+
 
 ### Getting started
+Tensorflow.js models have to be served through an URL to be loaded in javascript. The  Refine Net model is based on a main JSON file and shared weights files in a binary format. The idea is to create an HTTP server that will make the model available in a URL allowing requests and be thread as REST API. When loading the model, the TensorFlow.js will do the following requests:
+
+    GET /model.json
+    GET /group1-shard1of4
+    GET /group1-shard2of4
+    GET /group1-shard3of4
+    GET /group1-shard4of4
+
+The [http-server](https://www.npmjs.com/package/http-server) can be installed using the following command:
+
+`npm-install http-server -g`
+
+Once you have the program installed, go to the weights folder, run the command below and the model you be available at http://127.0.0.1:8080.
+
+`http-server -c1 --cors .`
+
+To install the required packages, it can be used `npm` or `yarn`. 
+
+If you are using  `yarn`:
+
+	yarn
+	yarn start
+
+If you are using  `npm`:
+
+	npm install
+	npm start
+
+To test the project just go to  [http://localhost:3000/] and the application will be running there.
+
+### References
+[1] Romera-Paredes, Bernardino, and Philip Hilaire Sean Torr. “Recurrent instance segmentation.” _European conference on computer vision_. Springer, Cham, 2016.
+
+[2] Lin, Guosheng, et al. “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation.” _Proceedings of the IEEE conference on computer vision and pattern recognition_. 2017.
+
+[3] Nekrasov, Vladimir, Chunhua Shen, and Ian Reid. “Light-weight refinenet for real-time semantic segmentation.” _arXiv preprint arXiv:1810.03272_ (2018).
+
+[4] Medium. 2020. _Introducing Tensorflow.Js: Machine Learning In Javascript_. [online] Available at: <https://medium.com/tensorflow/introducing-tensorflow-js-machine-learning-in-javascript-bf3eab376db> [Accessed 3 June 2020].
+
+[5] [https://www.tensorflow.org/js/tutorials/conversion/import_keras](https://www.tensorflow.org/js/tutorials/conversion/import_keras)
 
-TODO
+[6] [https://github.com/DrSleep/light-weight-refinenet](https://github.com/DrSleep/light-weight-refinenet)
 
+[7] [https://www.tensorflow.org/js/guide/conversion](https://www.tensorflow.org/js/guide/conversion)