This repository contains some models for semantic segmentation and the pipeline of training and testing models, implemented in PyTorch.
| Model | Backbone | Datasets | eval size | Mean IoU(paper) | Mean IoU(this repo) |
|---|---|---|---|---|---|
| DeepLabv3_plus | xception65 | cityscape(val) | (1025,2049) | 78.8 | 78.93 |
| DeepLabv3_plus | xception65 | coco(val) | 480/520 | - | 70.50 |
| DeepLabv3_plus | xception65 | pascal_aug(val) | 480/520 | - | 89.56 |
| DeepLabv3_plus | xception65 | pascal_voc(val) | 480/520 | - | 88.39 |
| DeepLabv3_plus | resnet101 | cityscape(val) | (1025,2049) | - | 78.27 |
| Danet | resnet101 | cityscape(val) | (1024,2048) | 79.9 | 79.34 |
| Pspnet | resnet101 | cityscape(val) | (1025,2049) | 78.63 | 77.00 |
| Model | Backbone | Datasets | eval size | Mean IoU(paper) | Mean IoU(this repo) | FPS |
|---|---|---|---|---|---|---|
| ICnet | resnet50(0.5) | cityscape(val) | (1024,2048) | 67.8 | - | 41.39 |
| DeepLabv3_plus | mobilenetV2 | cityscape(val) | (1024,2048) | 70.7 | 70.3 | 46.64 |
| BiSeNet | resnet18 | cityscape(val) | (1024,2048) | - | - | 39.90 |
| LEDNet | - | cityscape(val) | (1024,2048) | - | - | 31.78 |
| CGNet | - | cityscape(val) | (1024,2048) | - | - | 46.11 |
| HardNet | - | cityscape(val) | (1024,2048) | 75.9 | - | 69.06 |
| DFANet | xceptionA | cityscape(val) | (1024,2048) | 70.3 | - | 21.46 |
| HRNet | w18_small_v1 | cityscape(val) | (1024,2048) | 70.3 | 70.5 | 66.01 |
| Fast_SCNN | - | cityscape(val) | (1024,2048) | 68.3 | 68.9 | 145.77 |
FPS was tested on V100.
- python 3
- torch >= 1.1.0
- torchvision
- pyyaml
- Pillow
- numpy
python setup.py develop
if you do not want to run CCNet, you do not need to install, just comment following line in segmentron/models/__init__.py
from .ccnet import CCNet
Support cityscape, coco, voc, ade20k now.
Please refer to DATA_PREPARE.md for dataset preparation.
pretrained backbone models will be download automatically in pytorch default directory(~/.cache/torch/checkpoints/).
├── configs # yaml config file
├── segmentron # core code
├── tools # train eval code
└── datasets # put datasets here
CUDA_VISIBLE_DEVICES=0 python -u tools/train.py --config-file configs/cityscapes_deeplabv3_plus.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
You can download trained model from model zoo table above, or train by yourself.
CUDA_VISIBLE_DEVICES=0 python -u ./tools/eval.py --config-file configs/cityscapes_deeplabv3_plus.yaml \
TEST.TEST_MODEL_PATH your_test_model_path
CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh ${CONFIG_FILE} ${GPU_NUM} \
TEST.TEST_MODEL_PATH your_test_model_path
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