Create README.md

mrcnn/README.md

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+# Model Training
+
+## Table of Contents
+
+1. [Introduction](#introduction)  
+1. [Examples](#examples)  
+1. [Usage](#usage)  
+
+
+## Introduction
+
+
+## Setup
+
+Run data syntheszer module to generate the training  datasets in the following folder structure:
+
+~~~~~~~
+        project
+          |-- mrcnn
+               |-- scipts
+                    |-- config.py
+                    |-- model.py
+                    |-- train.py
+                    |-- pre_process.py
+                    |-- requirements.txt
+                    |-- utils.py
+                    |-- visualize.py
+                    |-- Inference_notebook.ipynb
+                    |-- utils.py
+
+               |--- mask_data
+                    |-- id_map.json
+                    |-- logs/
+                    |-- mask_rcnn_coco.h5
+                    |-- test_image
+                    |-- train_image
+~~~~~~~
+
+## Training
+
+
+### Pre-processing
+Data pre-processing using pre_process.py to generate .h5 file for masks.
+
+
+### Model and Training
+
+```
+ -- Modified Matterport's implementation of Mask-RCNN deep neural network for object instance segmentation.
+    Model - Added two new methods to train just specific masks.
+            mrcnn_mask: Just mask layers
+            mask_heads: Mask layers or rpn/fpn
+    Multiclass- Pre_processed images and mask files accordingly to prepare for multi classification.
+ -- Increased maximum number of predicted objects since an image can contain 200 or more bottles/bags/boxes.
+ -- Increased POST_NMS_ROIS_TRAINING to get more region proposals during training.
+ -- Resized images and masks to 512x512.
+ -- Used Default anchor size as we do not expect small objects: RPN_ANCHOR_SCALES = (16, 32, 64, 128, 256)
+ -- Relied heavily on deep image augmentation due to small training set:
+      Random horizontal or vertical flips
+      Random 90 or -90 degrees rotation
+      Random rotations in the range of (-20, 20) degrees
+      Random scaling of image and mask scaling in the range (0.5, 2.0)
+ -- Used Resnet101 architecture as a backbone encoder.
+ -- Trained the model with Adam optimizer for 65 epochs:
+ -- 5 epochs of heads with learning rate 1e-4 (To speed up the training process)
+ -- 30 epochs with learning rate 1e-5
+ -- 30 epochs with learning rate 1e-6
+ -- changed mAP computation to be (0.5 - 0.8) 
+ -- weighted mAP 
+ -- weighted loss 
+ LOSS_WEIGHTS = {
+        "rpn_class_loss": 20.,
+        "rpn_bbox_loss": 1.,
+        "mrcnn_class_loss": 10.,
+        "mrcnn_bbox_loss": 1.,
+        "mrcnn_mask_loss": 10.
+    }
+
+```
+
+### Model Execution and Run-Time
+Run python pre_process.py to pre-process  data 
+
+Run python train.py to train the model. Model weights are saved at ../data/logs/kaggle_bowl/mask_rcnn.h5.
+
+Run python inference_notebook.ipynb.py to evaluate model performance on test set 
+
+
+The following execution times are measured on Nvidia P100 GPUs provided by AWS Deep learning AMI
+
+```
+Each training epoch takes about 25 minutes.
+It takes about 18 hours to train the model from scratch.
+```
+
+## Example model predictions
+
+[put graphs from notebook]