Segmentation - Pascal VOC

Our segmentation code is based on pytorch-deeplab-xception.

Additional requirements

• tqdm
• matplotlib
• pillow

Settings

Teacher	Student	Teacher size	Student size	Size ratio
ResNet 101	ResNet 18	59.3M	16.6	28.0%
ResNet 101	MobileNetV2	59.3M	5.8M	9.8%

Teacher models

Download following pre-trained teacher network and put it into ./Segmentation/pretrained directory

• ResNet101-DeepLabV3+

We used pre-trained model in pytorch-deeplab-xception for teacher network.

Training

• First, move to segmentation folder : cd Segmentation

• Next, configure your dataset path on Segmentation/mypath.py

• Without distillation

  • ResNet 18

  CUDA_VISIBLE_DEVICES=0,1 python train.py --backbone resnet18 --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

  • MobileNetV2

  CUDA_VISIBLE_DEVICES=0,1 python train.py --backbone mobilenet --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

• Distillation

  • ResNet 18

  CUDA_VISIBLE_DEVICES=0,1 python train_with_distillation.py --backbone resnet18 --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

  -MobileNetV2

  CUDA_VISIBLE_DEVICES=0,1 python train_with_distillation.py --backbone mobilenet --gpu-ids 0,1 --dataset pascal --use-sbd --nesterov

Experimental results

This numbers are based validation performance of our code.

• ResNet 18

Network	Method	mIOU
ResNet 101	Teacher	77.89
ResNet 18	Original	72.07
ResNet 18	Proposed	73.98

• MobileNetV2

Network	Method	mIOU
ResNet 101	Teacher	77.89
MobileNetV2	Original	68.46
MobileNetV2	Proposed	71.19

In the paper, we reported performance on the test set, but our code measures the performance on the val set. Therefore, the performance on code is not same as the paper. If you want accurate measure, please measure performance on test set with Pascal VOC evaluation server.