IVP-RES

Referring Expression Segmentation with Instance-aware Visual Priors

Setup

We recommended the following dependencies.

• Python 3.6
• Numpy

Data Preparation

• Dataset Preprocessing

We conduct experiments on 4 datasets of referring image segmentation, including UNC, UNC+, Gref and ReferIt. After downloading these datasets, you can run the following commands for data preparation:

python build_batches.py -d Gref -t train
python build_batches.py -d Gref -t val
python build_batches.py -d unc -t train
python build_batches.py -d unc -t val
python build_batches.py -d unc -t testA
python build_batches.py -d unc -t testB
python build_batches.py -d unc+ -t train
python build_batches.py -d unc+ -t val
python build_batches.py -d unc+ -t testA
python build_batches.py -d unc+ -t testB
python build_batches.py -d referit -t trainval
python build_batches.py -d referit -t test

Training

Train on UNC training set with:

python train.py --config-file configs/my_Model_soft2-unc-3x.yaml --num-gpus 4

Testing

Test on UNC validation set with:

python train.py --config-file configs/my_Model_soft2-unc-3x.yaml --num-gpus 4 --eval-only MODEL.WEIGHTS ./output/my_Model_soft2_ab2/unc/1x/model_final.pth DATASETS.TEST \(\"unc_val\",\)

Pre-trained Models and Checkpoints

Pending...

Visualization

Get visualization results with:

python myvis.py --config-file configs/my_Model_soft2-unc-3x.yaml --num-gpus 1 MODEL.WEIGHTS ./output/my_Model_soft2_ab2/unc/1x/model_final.pth DATASETS.TEST \(\"unc_val\",\)

Citing IVP

Pending...