iMaterialist-2020
This is a segmentation framework modified from MaskRCNN.
requirements
- Python (>= 3.6)
- PyTorch
pip3 install -r requirements.txt
Demo
we provide a clothing parsing system using our trained model as segmentation model.
Train
This code support two types of model: without attribute and with attribute
./train.sh (lr schedule) (lr) # train without attr
#./train_attr.sh (attribute-wise weight) (attribute-loss weight) (lr) # train with attr
Val
predict on validation data (output mIOU and mF1)
./val.sh (experiment_name) (checkpoint_idx)
Test
test without attribute
# ./test.sh experiment_name mask_thresh checkpoint_idx
./test.sh torch_MaskRCNN_40e_lr0.1 0.5 21
test with attribute
# ./test_attr.sh experiment_name mask_thresh checkpoint_idx attr_score_thresh
./test_attr.sh torch_MaskRCNN_20e_lr0.01_attr_1000weight_3aweight 0.5 0 0.7
code log
train.py # train with train/val.csv, not support for online validation (validate while training)。[something wrong in code, seems to be the cuda device incorrespondence]
train_attr.py # train with attribute data，using binary cross entropy with specified pos weight and loss weight.