Ensemble of deep learning Super-resolution and interpolation in the Mask rcnn transform layer
placeholder (preparing...)
input
Mask-RCNN Caculates....
output
Mask rcnn transfrom layer Interpolation experiments
Before Image goes into mask rcnn backbone module, image is resized to fixed size to get good features. When resizing image, generally Bilinear interpolation is used. There're many kind of interpolation methods to resize in this repository code.
Train
main options
-
--dt : dataset
- pf : pennFudan
- bln : balloon
-
--model : interpolation method
- bicubic (👍)
- bilinear
- nearest
Usage
python train.py --dt pf --model bicubic -o ./model/something.pth
Secondary options
-
--out : default = './model/new.pth'
-
--epochs : default = 50
-
--batch : default = 4
-
--device : default = 'cuda:0'
-
--workers : default = 4
Evaluate
About performance
- mAP
- mask
- bbox
Jupyter (interactive)
: evaluate.ipynb
Shell
python evaluate.py -m /path/to/modelA.pth /path/to/modelB.pth -o /where/to/save/figure_dir
options
- -m ,--model (default) ./models/*.pth
- -o ,--output (default) false (false : prints evaluation results on console, true : saves graph images in ./results directory )
e.g )
python evaluate.py
python evaluate.py -m /path/to/modelA.pth /path/to/modelB.pth -o true
python evaluate.py -o true
python evaluate.py -m /path/to/modelA.pth /path/to/modelB.pth
image output : modelName_ap_epochs.jpg , modelName_ap_table.jpg
Inference
Adjust bicubic mask rcnn to your image.
Bash Usage
python inference.py -m ./models/pf_4_nearest.pth -i ./input.jpg -o ./output.jpg
options
- -m ,--model (default) './models/pf_4_bicubic.pth'
- -i ,--input (default) './sample/pds1.jpg'
- -o ,--output (default) './sample/pre_pds1.jpg'
ETC
If you have a question , feel free to ask me.