This is Homework 3 of VRDL about Instance segmentation for necleus dataset
| cocoapi
cocoAPI is a very powerful tool in Segmentation and Object detection
| mmdetection
This is a reference project from mmdetection
| --- model.sh
Command of model
| --- sub.sh
Command of zipping answer.json
| --- train_config
Pretrain model and config file are put in here
| --- tools
| ------ train.py
Main training program
| ------ test.py
Main inference program
| nucleus
Training and testing datasets should be put in here
| coco_trans.py
Program of Converting mask img
| data_collation.py
Shell script for data collation
| install.sh
Shell script for installing
Using python 3.7 in Anaconda
sh install.sh
Install all the dependencies
Download Nucleus dataset and put in nucleus
folder
Run python data_collation.py
for Collation all the data
Run python coco_trans.py
for Converting mask img into json file of coco
you must download pretrained R-101-FPN as backbone and put it in ./mmdetection/train_config
folder
Run sh model.sh train
for Training model
Run sh model.sh test [.pth file]
for inference
.json
file will be save in ./mmdetection/
you can run sh sub.sh {filename_zip}
for zipping file quickly
R-101-FPN as backbone, put it in ./mmdetection/traon_config
folder
Mask R-CNN-R-101-FPN is my model file, you can generate prediction result after checking Evaluation part
model name | mAP |
---|---|
Mask R-CNN-R-101-FPN (840*840) + RandomCrop (600 * 600) + anchor scale =8 | 0.24518 |
Mask R-CNN-R-101-FPN (840*840) + RandomCrop (600 * 600) + anchor scale =6 | 0.244608 |
Mask R-CNN-R-101-FPN (640*640) + anchor scale =8 | 0.221494 |