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LICENSE

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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright [2020] [Dhananjayan R]
+
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README.md

@@ -1,13 +1,107 @@
----
-title: Colozier
-emoji: 🔥
-colorFrom: green
-colorTo: red
-sdk: streamlit
-sdk_version: 1.34.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Colorizer
+Black and white image colorization with OpenCV.
+## Table of Content
+  * [Demo](#demo)
+  * [Overview](#overview)
+  * [Motivation](#motivation)
+  * [Technical Aspect](#technical-aspect)
+  * [Installation And Run](#installation-and-run)
+  * [Directory Tree](#directory-tree)
+  * [To Do](#to-do)
+  * [Bug / Feature Request](#bug---feature-request)
+  * [Technologies Used](#technologies-used)
+  * [Team](#team)
+  * [License](#license)
+  * [Credits](#credits)
+## Demo
+![Alt Text](https://j.gifs.com/2xVL2P.gif)
+
+## Overview
+Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input (for example, an ocean on a clear sunny day must be plausibly “blue” — it can’t be colored “hot pink” by the model).
+
+## Motivation
+
+When I learned linear algebra and came to know about how the machine inteprets pictures as tensors and concept of image segmentation. I remember there were some movies which was restored and picutured in theatre. I just came across Research papers of University of california in image colorization. And most iimportantly when I colorized photos of my Grandmother with gorgeous saree, that smile in my mother's face worth it.
+
+Here is a photo of Che guevara from 60's colorized:
+
+<img target="_blank" src="https://github.com/dhananjayan-r/Colorizer/blob/master/Input_images/che-guevara-wallpapers-hd-best-hd-photos-1080p-6xcp2u-741x988.jpg" width=300><img target="_blank" src="https://github.com/dhananjayan-r/Colorizer/blob/master/Result_images/colored_c1.jpg" width=300>
+
+## Technical Aspect
+- The technique we’ll be covering here today is from Zhang et al.’s 2016 ECCV paper, [Colorful Image Colorization](http://richzhang.github.io/colorization/). Developed at the University of California, Berkeley by Richard Zhang, Phillip Isola, and Alexei A. Efros.
+
+- Previous approaches to black and white image colorization relied on manual human annotation and often produced    desaturated results that were not “believable” as true colorizations.
+
+- Zhang et al. decided to attack the problem of image colorization by using Convolutional Neural Networks to  “hallucinate” what an input grayscale image would look like when colorized.
+
+- To train the network Zhang et al. started with the [ImageNet dataset](http://image-net.org/) and converted all images from the RGB color space to the Lab color space.
+
+- Similar to the RGB color space, the Lab color space has three channels. But unlike the RGB color space, Lab encodes color information differently:
+  - The **L channel** encodes lightness intensity only
+  - The **a channel** encodes green-red.
+  - And the **b channel** encodes blue-yellow.
+
+- As explained in the original paper, the authors, embraced the underlying uncertainty of the problem by posing it as a classification task using class-rebalancing at training time to increase the diversity of colors in the result. The Artificial Intelligent (AI) approach is implemented as a feed-forward pass in a CNN (“Convolutional Neural Network”) at test time and is trained on over a million color images.
+
+- The color photos were decomposed using Lab model and “L channel” is used as an input feature and “a and b channels” as classification labels as shown in below diagram.
+
+<img target="_blank" src="https://user-images.githubusercontent.com/71431013/99061015-eb844a80-25c6-11eb-8850-bcc9f74d91e6.png" width=500>
+
+- The trained model (that is available publically and in models folder of this repo or [download it by clicking here]( http://eecs.berkeley.edu/~rich.zhang/projects/2016_colorization/files/demo_v2/colorization_release_v2.caffemodel)), we can use it to colorize a new B&W photo, where this photo will be the input of the model or the component “L”. The output of the model will be the other components “a” and “b”, that once added to the original “L”, will return a full colorized image.
+
+## The entire (simplified) process can be summarized as:
+- Convert all training images from the RGB color space to the Lab color space.
+- Use the L channel as the input to the network and train the network to predict the ab channels.
+- Combine the input L channel with the predicted ab channels.
+- Convert the Lab image back to RGB.
+
+<img target="_blank" src="https://user-images.githubusercontent.com/71431013/99061033-f048fe80-25c6-11eb-8bc5-d6312c7021b6.png" width=500>
+
+## Installation And Run 
+1.The Code is written in Python 3.7. If you don't have Python installed you can find it [here](https://www.python.org/downloads/). If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after [cloning](https://www.howtogeek.com/451360/how-to-clone-a-github-repository/) the repository:
+```bash
+pip install -r requirements.txt
+```
+2. [Run the file with](https://docs.streamlit.io/en/stable/):
+ ```bash
+ $ streamlit run app.py
+ ```
+ 
+## Directory Tree 
+```   
+|   app.py  
++---Input_images
+|       che-guevara-.jpg
+|       pexels-pixabay-141651.jpg
+|       pexels-pixabay-36755.jpg      
++---models
+|       colorization_release_v2.caffemodel
+|       models_colorization_deploy_v2.prototxt
+|       pts_in_hull.npy     
+\---Result_images
+        colored_c1.jpg
+        colored_c7.jpg
+        colored_c8.jpg
+```
+## To Do
+- To Convert the application to colorize black and white videos.
+
+## Bug / Feature Request
+If you find a bug , kindly open an issue [here](https://github.com/dhananjayan-r/Colorizer/issues) by including your search query and the expected result.
+
+If you'd like to request a new function, feel free to do so by opening an issue [here](https://github.com/dhananjayan-r/Colorizer/issues). Please include sample queries and their corresponding results.
+
+## Technologies Used
+![](https://forthebadge.com/images/badges/made-with-python.svg)
+
+[<img target="_blank" src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/32/OpenCV_Logo_with_text_svg_version.svg/730px-OpenCV_Logo_with_text_svg_version.svg.png" width=200>](https://opencv.org/)[<img target="_blank" src="https://miro.medium.com/max/4000/0*cSCGhssjeajRD3qs.png" width=200>](https://www.streamlit.io/)
+
+## Team
+[<img target="_blank" src="" width=200>](www.linkedin.com/in/vivek-vari/)|
+-|
+[vivek](www.linkedin.com/in/vivek-vari/) |)
+
+
+## Credits
+- [“ Black and white image colorization with OpenCV and Deep Learning” by Dr. Adrian Rosebrok "](https://www.pyimagesearch.com/2019/02/25/black-and-white-image-colorization-with-opencv-and-deep-learning/) - This project wouldn't have been possible without these references.
+- [The official publication of Zhang et al.](http://richzhang.github.io/colorization/)

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[6]:
+
+
+
+# import the necessary packages
+import numpy as np
+import cv2
+import streamlit as st
+from PIL import Image
+
+
+
+def colorizer(img):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    # load our serialized black and white colorizer model and cluster
+    # center points from disk
+    #Note: Please take in account the directories of your local system.
+    prototxt = r"C:\Users\dhananjayan\projects\Colorizer\models\models_colorization_deploy_v2.prototxt"
+    model = r"C:\Users\dhananjayan\projects\Colorizer\models\colorization_release_v2.caffemodel"
+    points = r"C:\Users\dhananjayan\projects\Colorizer\models\pts_in_hull.npy"
+    net = cv2.dnn.readNetFromCaffe(prototxt, model)
+    pts = np.load(points)
+    # add the cluster centers as 1x1 convolutions to the model
+    class8 = net.getLayerId("class8_ab")
+    conv8 = net.getLayerId("conv8_313_rh")
+    pts = pts.transpose().reshape(2, 313, 1, 1)
+    net.getLayer(class8).blobs = [pts.astype("float32")]
+    net.getLayer(conv8).blobs = [np.full([1, 313], 2.606, dtype="float32")]
+    # scale the pixel intensities to the range [0, 1], and then convert the image from the BGR to Lab color space
+    scaled = img.astype("float32") / 255.0
+    lab = cv2.cvtColor(scaled, cv2.COLOR_RGB2LAB)
+    # resize the Lab image to 224x224 (the dimensions the colorization
+    #network accepts), split channels, extract the 'L' channel, and then perform mean centering
+    resized = cv2.resize(lab, (224, 224))
+    L = cv2.split(resized)[0]
+    L -= 50
+    # pass the L channel through the network which will *predict* the 'a' and 'b' channel values
+    net.setInput(cv2.dnn.blobFromImage(L))
+    ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
+    # resize the predicted 'ab' volume to the same dimensions as our input image
+    ab = cv2.resize(ab, (img.shape[1], img.shape[0]))
+    # grab the 'L' channel from the *original* input image (not the
+    # resized one) and concatenate the original 'L' channel with the predicted 'ab' channels
+    L = cv2.split(lab)[0]
+    colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
+    # convert the output image from the Lab color space to RGB, then clip any values that fall outside the range [0, 1]
+    colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2RGB)
+    colorized = np.clip(colorized, 0, 1)
+    # the current colorized image is represented as a floating point
+    # data type in the range [0, 1] -- let's convert to an unsigned 8-bit integer representation in the range [0, 255]
+    colorized = (255 * colorized).astype("uint8")
+    # Return the colorized images
+    return colorized
+
+##########################################################################################################
+    
+st.write("""
+          # Colorize your Black and white image
+          """
+          )
+
+st.write("This is an app to turn Colorize your B&W images.")
+st.write("Created on Thursday, 12 November 2020 (IST) \n @author: Dhananjayan")
+
+file = st.sidebar.file_uploader("Please upload an image file", type=["jpg", "png"])
+
+if file is None:
+    st.text("You haven't uploaded an image file")
+else:
+    image = Image.open(file)
+    img = np.array(image)
+    
+    st.text("Your original image")
+    st.image(image, use_column_width=True)
+    
+    st.text("Your colorized image")
+    color = colorizer(img)
+    
+    st.image(color, use_column_width=True)
+    
+    print("done!")
+
+
+# In[ ]:
+
+
+
+

models/colorization_release_v2.caffemodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a0b0ec746f8f3100a7f14f6e18176032300123d9bebe79a7081fa9f1b7a9cbe
+size 134

models/models_colorization_deploy_v2.prototxt

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+name: "LtoAB"
+
+layer {
+  name: "data_l"
+  type: "Input"
+  top: "data_l"
+  input_param {
+    shape { dim: 1 dim: 1 dim: 224 dim: 224 }
+  }
+}
+
+# *****************
+# ***** conv1 *****
+# *****************
+layer {
+  name: "bw_conv1_1"
+  type: "Convolution"
+  bottom: "data_l"
+  top: "conv1_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "conv1_2norm"
+  type: "BatchNorm"
+  bottom: "conv1_2"
+  top: "conv1_2norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv2 *****
+# *****************
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  # bottom: "conv1_2"
+  bottom: "conv1_2norm"
+  # bottom: "pool1"
+  top: "conv2_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "conv2_2norm"
+  type: "BatchNorm"
+  bottom: "conv2_2"
+  top: "conv2_2norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv3 *****
+# *****************
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  # bottom: "conv2_2"
+  bottom: "conv2_2norm"
+  # bottom: "pool2"
+  top: "conv3_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_3norm"
+  type: "BatchNorm"
+  bottom: "conv3_3"
+  top: "conv3_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv4 *****
+# *****************
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  # bottom: "conv3_3"
+  bottom: "conv3_3norm"
+  # bottom: "pool3"
+  top: "conv4_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "conv4_3norm"
+  type: "BatchNorm"
+  bottom: "conv4_3"
+  top: "conv4_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv5 *****
+# *****************
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  # bottom: "conv4_3"
+  bottom: "conv4_3norm"
+  # bottom: "pool4"
+  top: "conv5_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0}   
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "conv5_3norm"
+  type: "BatchNorm"
+  bottom: "conv5_3"
+  top: "conv5_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv6 *****
+# *****************
+layer {
+  name: "conv6_1"
+  type: "Convolution"
+  bottom: "conv5_3norm"
+  top: "conv6_1"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_1"
+  type: "ReLU"
+  bottom: "conv6_1"
+  top: "conv6_1"
+}
+layer {
+  name: "conv6_2"
+  type: "Convolution"
+  bottom: "conv6_1"
+  top: "conv6_2"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_2"
+  type: "ReLU"
+  bottom: "conv6_2"
+  top: "conv6_2"
+}
+layer {
+  name: "conv6_3"
+  type: "Convolution"
+  bottom: "conv6_2"
+  top: "conv6_3"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_3"
+  type: "ReLU"
+  bottom: "conv6_3"
+  top: "conv6_3"
+}
+layer {
+  name: "conv6_3norm"
+  type: "BatchNorm"
+  bottom: "conv6_3"
+  top: "conv6_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv7 *****
+# *****************
+layer {
+  name: "conv7_1"
+  type: "Convolution"
+  bottom: "conv6_3norm"
+  top: "conv7_1"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_1"
+  type: "ReLU"
+  bottom: "conv7_1"
+  top: "conv7_1"
+}
+layer {
+  name: "conv7_2"
+  type: "Convolution"
+  bottom: "conv7_1"
+  top: "conv7_2"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_2"
+  type: "ReLU"
+  bottom: "conv7_2"
+  top: "conv7_2"
+}
+layer {
+  name: "conv7_3"
+  type: "Convolution"
+  bottom: "conv7_2"
+  top: "conv7_3"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_3"
+  type: "ReLU"
+  bottom: "conv7_3"
+  top: "conv7_3"
+}
+layer {
+  name: "conv7_3norm"
+  type: "BatchNorm"
+  bottom: "conv7_3"
+  top: "conv7_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv8 *****
+# *****************
+layer {
+  name: "conv8_1"
+  type: "Deconvolution"
+  bottom: "conv7_3norm"
+  top: "conv8_1"
+  convolution_param {
+    num_output: 256
+    kernel_size: 4
+    pad: 1
+    dilation: 1
+    stride: 2
+  }
+}
+layer {
+  name: "relu8_1"
+  type: "ReLU"
+  bottom: "conv8_1"
+  top: "conv8_1"
+}
+layer {
+  name: "conv8_2"
+  type: "Convolution"
+  bottom: "conv8_1"
+  top: "conv8_2"
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu8_2"
+  type: "ReLU"
+  bottom: "conv8_2"
+  top: "conv8_2"
+}
+layer {
+  name: "conv8_3"
+  type: "Convolution"
+  bottom: "conv8_2"
+  top: "conv8_3"
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu8_3"
+  type: "ReLU"
+  bottom: "conv8_3"
+  top: "conv8_3"
+}
+# *******************
+# ***** Softmax *****
+# *******************
+layer {
+  name: "conv8_313"
+  type: "Convolution"
+  bottom: "conv8_3"
+  top: "conv8_313"
+  convolution_param {
+    num_output: 313
+    kernel_size: 1
+    stride: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "conv8_313_rh"
+  type: "Scale"
+  bottom: "conv8_313"
+  top: "conv8_313_rh"
+  scale_param {
+    bias_term: false
+    filler {      type: 'constant'      value: 2.606    }
+  }
+}
+layer {
+  name: "class8_313_rh"
+  type: "Softmax"
+  bottom: "conv8_313_rh"
+  top: "class8_313_rh"
+}
+# ********************
+# ***** Decoding *****
+# ********************
+layer {
+  name: "class8_ab"
+  type: "Convolution"
+  bottom: "class8_313_rh"
+  top: "class8_ab"
+  convolution_param {
+    num_output: 2
+    kernel_size: 1
+    stride: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "Silence"
+  type: "Silence"
+  bottom: "class8_ab"
+}