add Readme

Author: aelbouchti

.gitignore

@@ -8,6 +8,7 @@ __pycache__/
 
 # Distribution / packaging
 .Python
+env/
 research/
 build/
 develop-eggs/

README.md

@@ -1,42 +1,27 @@
 # Models-implementation
-This is an implementation of CNN's architectures using TensorFlow. The main goal of this project is to develop solutions for healthcare, based on machine learning & deep learning.
+This is an implementation of CNN's architectures using TensorFlow. The main goal of this project is to support the creation of machine learning & deep learning Pipelines .
 
 [lien](./setup.py)
 
 ## Description
 The repository is composed of:
 - Scripts for launching training and evaluation: It includes the basic tf.Session() and tf.train.MonitoredSession with different hooks and scaffold for distributed training.
 - Folder Densenet: Regroup image preprocessing functions, script for a slim-like implementation of Densenet
-- Folder utils: Convert data to Tfrecord format, analyse your data with statistics.py
+- Folder utils: Convert data to Tfrecord format, analyse your data depending on its nature
 
-## Usage example
-
-
-## Release History
-  
-*name*
-**name**
-~~sssss~~
+##Installation
 
-`print(a.0)`
+First, install Tensorflow (Or/And tensorflow-gpu) in order to perform computation in cpu-only (In GPU) 
+Then, in order to use "slim" package, which is developped under : [lien](https://www.github.com/tensorflow/models/research/slim), 
+git clone the above repository. Copy the "research" folder and perform both setup.py : 
+- under research folder
+- under slim folder (make sure you delete BUILD file before running setup.py)
 
-```markdown
-import sys
-print(sys.argv)
-```
+As a last step, git clone Models-implementation repo : 
+https://www.github.com/medtune/Models-implementation
 
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+## Usage example
 
-# titre 2
 
-| e | eezaeazeaz |
-| --- | --- |
-| ezaeazeeaze | eazeazeaz |
+## Release History

yaml/config/config.yaml

@@ -30,7 +30,7 @@ image_size : 224
 #Nombre d'époques pour l'entraînement
 num_epochs : 100
 #State your batch size
-batch_size : 8
+batch_size : 16
 #Learning rate information and configuration (Up to you to experiment)
 initial_learning_rate : 0.0001
 #Decay factor