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https://github.com/tonysy/deep-feature-flow-segmentation
Deep Feature Flow for Video Semantic Segmentation
https://github.com/tonysy/deep-feature-flow-segmentation
artificial-intelligence deep-learning mxnet sematic-segmentation video
Last synced: 16 days ago
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Deep Feature Flow for Video Semantic Segmentation
- Host: GitHub
- URL: https://github.com/tonysy/deep-feature-flow-segmentation
- Owner: tonysy
- License: mit
- Created: 2017-12-26T17:15:41.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T21:23:49.000Z (over 2 years ago)
- Last Synced: 2024-10-11T18:14:05.497Z (about 1 month ago)
- Topics: artificial-intelligence, deep-learning, mxnet, sematic-segmentation, video
- Language: Python
- Homepage:
- Size: 235 KB
- Stars: 35
- Watchers: 5
- Forks: 4
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Feature Flow for Video Semantic Segmentation
Based on Deeplab V2
## 1. Setup environment
- If you use our dockerfile, you can run the code easily.
- If you want to set up your own env, please follow these steps:
- We only support `python2.7` now
- Install tk: `sudo apt-get -y install python-tk`
- Install OpenCV 3.4.1
- Install needed python packages with `pip install -r requirements.txt`
- If you are in China Mainland, you can use these to speedup
`pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple`
- Then `sh init.sh` to build the lib for faster-rcnn
Because we use the code from Deformable ConvNets and the dataloader has some dependencies on faster-rcnn, so you need to build the lib first.
## 2. Prepare Data and Pretrained Model
### Cityscapes Data
You need to download the cityscapes data from the official webpapge and unzip the data
Put the data into `data/cityscapes`, you can use soft link to set the data path as the following:
`ln -s Dataset_path ./data/cityscapes`
If you want to try DFF, you should download cityscapes video data and put it into `data/cityscapes_video`
### Pretrained Model
Download pretrained resnet model flow net from [Onedrive](https://onedrive.live.com/?authkey=%21AAXQgYjWim3Iz6w&cid=F371D9563727B96F&id=F371D9563727B96F%21102798&parId=F371D9563727B96F%21102795&action=locate), and put the model into `mode/pretrained_model/`
```bash
./model/pretrained_model/resnet_v1_101-0000.params
./model/pretrained_model/flownet-0000.params
```
## 3. Train and Test
### Training Deeplab V2
`python ./experiments/deeplab/deeplab_train_test.py --cfg ./experiments/deeplab/cfgs/deeplab_resnet_v1_101_cityscapes_segmentation_base.yaml`
### Training Deeplab V2 Deformable
`python ./experiments/deeplab/deeplab_train_test.py --cfg ./experiments/deeplab/cfgs/deeplab_resnet_v1_101_cityscapes_segmentation_dcn.yaml`
### Training DFF Deeplab V2
`python ./experiments/deeplab_dff/deeplab_dff_train.py --cfg ./experiments/deeplab_dff/cfgs/deeplab_resnet_v1_101_cityscapes_segmentation_video.yaml`
## 4. Performance
TBD
## 5. TODO List
- [x] Add Scripts
- [ ] Add experiment results
- [ ] Add support for Deeplab V3+
- [ ] Add BiSeNet
## 6. FAQ
- Program hang if your system opencv is 2.x and your opencv-python is 3.x
## 7. Acknowledgement
Thanks for the official deep featuere flow implementation and deeplab implementation from MSRACVER
- [Deep Feature Flow](https://github.com/msracver/Deep-Feature-Flow)
- [Deformable ConvNets](https://github.com/msracver/Deformable-ConvNets)