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Implementation of Mix-and-Match Tuning for Self-Supervised Semantic Segmentation.


Xiaohang Zhan, Ziwei Liu, Ping Luo, Xiaoou Tang, Chen Change Loy, "Mix-and-Match Tuning for Self-Supervised Semantic Segmentation", AAAI 2018

Project Page: link


Library (Note that the versions are not strictly restricted): OpenMPI=1.8.5, CUDA=8.0, CUDNN=5.1.10

Python: cv2

Before Start

  1. Download pre-trained models in link to pretrain. (You can also find trained graph_iter_xxx.caffemodel which can directly be fine-tuned for segmentation.)

  2. Download PASCAL VOC 2012 augmented dataset and CityScapes dataset to a proper position.

    For PASCAL VOC 2012, create standard training list as shown in data/pascal/train.txt and validation list as shown in data/pascal/val.txt

    For CityScapes, create standard training list as shown in data/cityscapes/train.txt and validation list as shown in data/cityscapes/val.txt

  3. Build caffe with cmake

    cd caffe


For example, train alexnet with colorization as pretrained model.

cd Alexnet/colorize

Then edit train_graph.prototxt and finetune_seg.prototxt to specify "source" and "root_dir" in the data layer.

sh # or use trained models in the Google Drive folder.


Edit to specify data root, testing list and ground truth root.


Testing results are saved in snapshot/seg_iter_xxx/ by default.

Citing Mix-and-Match

 author = {Xiaohang Zhan, Ziwei Liu, Ping Luo, Xiaoou Tang, and Chen Change Loy},
 title = {Mix-and-Match Tuning for Self-Supervised Semantic Segmentation},
 booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
 month = {February},
 year = {2018} 


The CAFFE is forked from