hurueilin / VRDL_HW3

VRDL HW3 - Nuclei Segmentation

tags: 基於深度學習之視覺辨識專論

Introduction

In this assignment, we are given we are given 24 training images and 6 test images. Each image contains multiple nucleus. Our goal is to train an instance segmentation model to detect and segment all nuclei in the image.

I use matterport/Mask_RCNN as my code base. In this repository, I include the main code myNucleus.py only. You need to install matterport/Mask_RCNN first and then paste my code into it. See Installation part for more detail.

Environment

Hardware

• CPU: Intel i5-7500 CPU
• GPU: NVIDIA GeForce GTX 1060 6GB

Installation

1. Refer to the installaton instructions on matterport/Mask_RCNN project. The following are the installation steps based on my environment FYI.

   conda create --name TF(1.15) python=3.7
   
   conda install cython
   pip install opencv-python
   conda install -c anaconda pillow
   conda install -c anaconda scikit-image
   conda install imgaug
   
   
   去NVIDIA裝CUDA10.0 & 拖曳cudnn到bin/include/lib資料夾
   conda install cudatoolkit=10.0 
   conda install cudnn=7.6.5 
   
   pip install h5py==2.10.0
   pip install tensorflow==1.15.0
   pip install tensorflow-gpu==1.15.0
   pip install keras==2.1.6
   
   
   裝pycocotools
   pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
   
   cd進Mask_RCNN，執行
   python setup.py install
   
   conda install -c anaconda ipykernel (最後再裝)
   (在VS code中遇到ipykernel無法執行，先conda uninstall traitlets，再重新裝ipykernel)
   

   If everything goes fine, you should be able to run samples/demo.ipynb successfully.

2. Put myNucleus.py into Mask_RCNN/samples/nucleus folder.

Prepare dataset

1. Create a folder named myDataset.
2. Create folder myTrain and myTest, then put training data and testing data into it.

Organize training like this:

Organize testing data like this:

Train & Test

Make sure you activate the conda environment, and cd to Mask_RCNN/samples/nucleus first.

Train

Run this command to train from COCO pre-trained weights.

python myNucleus.py train --dataset=myDataset --subset=myTrain --weights=coco

Test (Inference)

To test the model and generate answer.json, create a folder named output and put mask_rcnn_nucleus_0050.h5 inside. Then, run this command:

python myNucleus.py detect --dataset=myDataset --subset=myTest --weights=output/mask_rcnn_nucleus_0050.h5

After execution, it will generate answer.json file under samples/nucleus.

Results

(Refer to the report for more details.)

Model Name	Backbone	Best score on CodaLab
mask_rcnn_nucleus_0050.h5	ResNet50	0.244301