This project uses the MaskRCNN network to slove the semantic segmentation challenge of Tiny Poscal dataset. The model is base on pytorch and torchvision library and reference to this tutorial
The following specs were used to create the original solution.
- Ubuntu 18.04 LTS
- Intel(R) Core(TM) i5-9600K CPU @ 3.70GHz
- NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)
All requirements should be detailed in requirements.txt.
# python version: Python 3.6.9
pip3 install -r requirements.txt
- download the training data from here google drive
- To training this model on our own dataset, we must follow the format defined here and it was done here,
dataloader.py
. - By default, I set "./dataset" as the root directory of our dataset. You can set a symbolic link to the real path or pass the real path as a parameter.
- Using the following script to get more information
$ python train.py --help
- Example
python3 train.py --lr 0.001 --dataset "./dataset"
- Add parameter,
--test
to forward the test data and--save_json
to generate the prediction file with coco style format.
$ python train.py --weight <PATH_TO_TRAINED_MODEL_WEIGHT> --test --save_json
- The result would save under the
submissions
folder.