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getting_started.md

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Getting Started

Data Preparation

Download the data (VOC, Cityscapes) and pre-trained ResNet models from OneDrive link:
DATA/
|-- city
|-- pascal_voc
|-- pytorch-weight
|   |-- resnet50_v1c.pth
|   |-- resnet101_v1c.pth

Training && Inference on PASCAL VOC:

$ cd ./model/voc8.res50v3+.CPS
$ bash script.sh
  • The tensorboard file is saved in log/tb/ directory.
  • In script.sh, you need to specify some variables, such as the path to your data dir, the path to your snapshot dir that stores checkpoints, etc.
  • We have released the training log and pretrained model for this experiment on OneDrive. The performance is slightly different (73.28) from that of paper (73.20) due to randomness.
  • We have also released the training log of city8.res50v3+.CPS.

Different Partitions

To try other data partitions beside 1/8, you just need to change two variables in config.py:

C.labeled_ratio = 8
C.nepochs = 34

Please note that, for fair comparison, we control the total iterations during training in each experiment similar (almost the same), including the supervised baseline and semi-supervised methods. Therefore, the nepochs for different partitions are different.

We take VOC as an example.

  1. We totally have 10582 images. The full supervised baseline is trained for 60 epochs with batch size 16, thus having 10582*60/16 = 39682.5 iters.
  2. If we train CPS under the 1/8 split, we have 1323 labeled images and 9259 unlabeled images. Since the number of unlabeled images is larger than the number of labeled images, the epoch is defined as passing all the unlabeled images to the network. In each iteration, we have 8 labeled images and 8 unlabeled images, thus having 9259/8 = 1157.375 iters in one epoch. Then the total epochs we need is 39682.5/1157.375 = 34.29 ≈ 34.
  3. For the supervised baseline under the 1/8 split, the batch size 8 (as illustrated in Appendix A in the paper) and the iteration number is 39682.5 (the same as semi-supervised method).

We list the nepochs for different datasets and partitions in the below.

Dataset 1/16 1/8 1/4 1/2
VOC 32 34 40 60
Cityscapes 128 137 160 240