This demo downloads a pretrained model automatically if a pretrained model path is not given.
$ python demo.py [--dataset cityscapes|ade20k] [--gpu <gpu>] [--pretrained-model <model_path>] [--input-size <size>] <image>.jpg
Convert *.caffemodel to *.npz. Some layers are renamed to fit ChainerCV.
$ python caffe2npz.py <source>.caffemodel <target>.npz
The converted weight can be downloaded from here.
The performance on the Cityscapes dataset is as follows with single scale inference. Scores are measured by mean Intersection over Union (mIoU).
| Dataset | Scale | Original | Ours (weight conversion) |
|---|---|---|---|
| Cityscapes | Single scale | 79.70 % [1] | 79.03 % |
The model can be trained with a script train_mutli.py.
The following table shows the performance of the models trained with our scripts.
| Model | Training Data | Original | Ours |
|---|---|---|---|
| PSPNet w/ Dilated ResNet50 | fine only (3K) | 76.9 % [2] | 73.99 % |
| PSPNet w/ Dilated ResNet101 | fine only (3K) | 77.9 % [2] | 76.01 % |
Here are the commands used to train the models included in the table.
$ mpiexec -n 8 python3 train_multi.py --dataset cityscapes --model pspnet_resnet50 --iteration 90000
$ mpiexec -n 8 python3 train_multi.py --dataset cityscapes --model pspnet_resnet101 --iteration 90000
The following table shows the performance of the models trained with our scripts.
| Model | Original | Ours |
|---|---|---|
| PSPNet w/ Dilated ResNet50 | 41.68 % [1] | 34.97 % |
| PSPNet w/ Dilated ResNet101 | 36.55 % |
Here are the commands used to train the models included in the table.
$ mpiexec -n 8 python3 train_multi.py --dataset ade20k --model pspnet_resnet50 --iteration 150000
$ mpiexec -n 8 python3 train_multi.py --dataset ade20k --model pspnet_resnet101 --iteration 150000
The evaluation can be conducted using chainercv/examples/semantic_segmentation/eval_semantic_segmentation.py.
- Hengshuang Zhao et al. "Pyramid Scene Parsing Network" CVPR 2017.
- https://github.com/holyseven/PSPNet-TF-Reproduce (Validation scores for Cityscapes are lacking in the original paper)
- chainer-pspnet by mitmul