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https://github.com/z-x-yang/cfbi

The official implementation of CFBI(+): Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration.
https://github.com/z-x-yang/cfbi

computer-vision pytorch-implementation video-object-segmentation

Last synced: 5 days ago
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The official implementation of CFBI(+): Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration.

Host: GitHub
URL: https://github.com/z-x-yang/cfbi
Owner: z-x-yang
License: bsd-3-clause
Created: 2020-03-15T03:47:17.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2023-01-18T12:58:50.000Z (almost 2 years ago)
Last Synced: 2023-11-07T14:17:48.097Z (about 1 year ago)
Topics: computer-vision, pytorch-implementation, video-object-segmentation
Language: Python
Homepage:
Size: 3.78 MB
Stars: 319
Watchers: 20
Forks: 44
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # CFBI(+): Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration

The official implementation of Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration. [[paper](http://arxiv.org/abs/2010.06349)] (TPAMI) [[paper](https://arxiv.org/abs/2003.08333)] (ECCV 2020, Spotlight)  [[demo (YouTube)](https://www.youtube.com/watch?v=xdHi68UFt50)][[demo (Bilibili)](https://www.bilibili.com/video/av456456973/)]

For VOS-related applications or research, we also recommend our latest VOS framework, [AOT (NeurIPS 2021)](https://github.com/z-x-yang/AOT), whose performance and efficiency are much better than CFBI(+).

**Based on some necessary considerations, we only release the inference here. If you want to get the training code of CFBI, please contact us by email: . And please inform us of your institution and the purpose of using CFBI in the email. Thank you for your understanding!**

Framework:



Some video segmentation results:



## Requirements

    1. Python3

    2. pytorch >= 1.3.0 and torchvision

    3. opencv-python and Pillow

## Getting Started

1. Prepare datasets:

    * Download the [validation split](https://drive.google.com/file/d/1-QrceIl5sUNTKz7Iq0UsWC6NLZq7girr/view?usp=sharing) of YouTube-VOS 2018, and decompress the file to `datasets/YTB/valid`. If you want to evaluate CFBI on YouTube-VOS 2019, please download this [split](https://drive.google.com/file/d/1o586Wjya-f2ohxYf9C1RlRH-gkrzGS8t/view?usp=sharing) instead.

    * Download 480p [TrainVal](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-trainval-480p.zip) split of DAVIS 2017, and decompress the file to `datasets/DAVIS`.

2. Evaluating:

    * **YouTube-VOS**: Download pretrained [CFBI](https://drive.google.com/file/d/1YKQL8ruEfcDvKPO5Rs3d6HfVXAv08hX6/view?usp=share_link) or [CFBI+](https://drive.google.com/file/d/1tEg9sy6LYEXQP8QLsagmXpxYc9D3X5Vc/view?usp=share_link), to `pretrain_models`, and then run `bash ytb_eval.sh` for CFBI or `bash ytb_eval_cfbip.sh` for CFBI+. After the evaluation, the result will be packed into a Zip file, which you need to send to [official evaluation server](https://competitions.codalab.org/competitions/19544) to calculate a score. For 2019 version, use this [server](https://competitions.codalab.org/competitions/20127) instead. The pretrained CFBI has been trained on YouTube-VOS using a larger batch size (16) and learning rate (0.02), which boosts the performance (J&F) to `81.8%` on the validation split of YouTube-VOS 2018.

    * **DAVIS**: Download pretrained [CFBI](https://drive.google.com/file/d/1G6tVaaU4RYKXKKFxqxdE_p8Fwju_0nVE/view?usp=share_link) or [CFBI+](https://drive.google.com/file/d/1KkU-Dxew1wI1affTjN1LmTld0kgpUUQH/view?usp=share_link), to `pretrain_models`, and then run `bash davis_eval.sh` for CFBI or `bash davis_eval_cfbip.sh` for CFBI+. After the evaluation, please use [official code](https://github.com/davisvideochallenge/davis2017-evaluation) to calculate a score, which should be `81.9%` or `83.0%` (J&F).

    * **Fast CFBI**: To reduce memory usage, we also provide a fast setting in `ytb_eval_fast.sh`. The fast setting enables using `float16` in the matching process of CFBI. Besides, we apply an `atrous strategy` in the global matching of CFBI for further efficiency (The discussion of atrous matching will be submitted to our Arxiv paper soon). The fast setting will save a large amount of memory and significantly improve the inference speed of CFBI. However, this will only lose very little performance.

    * Another way for saving memory is to increase the number of `--global_chunks`. This will not affect performance but will make the network speed slightly slower.

## Model Zoo

**We recorded the inference speed of CFBI by using one NVIDIA Tesla V100 GPU. Besides, we used a multi-object speed instead of a single-object. A large portion of the sequences in VOS datasets contains multiple objects, and CFBI is good at processing multiple objects simultaneously.**

`F16` denotes using `float16` in the matching process. `Fast` means using both `float16` and `atrous strategy` in the inference stage. `MS` denotes using a multi-scale and flip strategy during inference (additional evaluation args `--ms 1. 1.15 1.3 1.5 --flip`).

**YouTube-VOS** (Eval on Val 2018):

In the inference stage, we restricted the long edge of each frame to be no more than 1040 (1.3 * 800) pixels, which is consistent with the biggest random-scale size (1.3 * 480p) in the training stage and is smaller than the original size of YouTube-VOS (720p).

**Name** | **Backbone**  | **J Seen** | **F Seen** | **J Unseen** | **F Unseen** | **Mean** | **Multi-Obj** 
 **FPS** | **Link** 

---------| :-----------: | :--------: | :--------: | :----------: | :----------: | :------: | :------------------------: | :------:

ResNet101-CFBI+ | ResNet101-DeepLabV3+ | 81.8 | **86.6** | **77.1** | **85.6** | **82.8** | 4.03 | [Click](https://drive.google.com/file/d/1tEg9sy6LYEXQP8QLsagmXpxYc9D3X5Vc/view?usp=share_link)

ResNet101-MS-CFBI+ | ResNet101-DeepLabV3+ | **82.8** | **87.5** | **77.3** | **85.7** | **83.3** | 0.48 (88.1%↓) | The same as above

ResNet101-F16-CFBI+ | ResNet101-DeepLabV3+ | **81.9** | **86.6** | **77.1** | **85.6** | **82.8** | 4.93 (22.3%↑) | The same as above

ResNet101-Fast-CFBI+ | ResNet101-DeepLabV3+ | **81.9** | **86.6** | **77.1** | 85.5 | **82.8** | 5.06 (25.6%↑) | The same as above

ResNet101-CFBI | ResNet101-DeepLabV3+ | **81.9** | 86.3 | 75.6 | 83.4 | 81.8 | 3.48 | [Click](https://drive.google.com/file/d/1YKQL8ruEfcDvKPO5Rs3d6HfVXAv08hX6/view?usp=share_link) 

ResNet101-F16-CFBI | ResNet101-DeepLabV3+ |**81.9** | 86.4 | 75.6 | 83.3 | 81.8 | 4.62 (32.8%↑) | The same as above

ResNet101-Fast-CFBI | ResNet101-DeepLabV3+ | **81.9** | 86.4 | 75.6 | 83.1 |81.8 | **7.61 (118.7%↑)** | The same as above

MobileNetV2-CFBI | MobileNetV2-DeepLabV3+ | 80.4 | 84.7 | 74.9 | 82.6 | 80.6 | 3.88 | [Click](https://drive.google.com/file/d/1h8P7LrcrOmKo2Yet_LLf6XaM97diCL42/view?usp=share_link)

MobileNetV2-Fast-CFBI | MobileNetV2-DeepLabV3+ | 80.2 | 84.6 | 74.7 | 82.7 | 80.6 | **9.69 (150.0↑%)** | The same as above

**DAVIS** (Eval on Val 2017):

In the inference stage, we ran using the default size of DAVIS (480p).

**Name** | **Backbone**  | **J score** | **F score** | **Mean** | **Multi-Obj** 
 **FPS** | **Link** 

---------| :-----------: | :---------: | :---------: | :------: | :------------------------: | :------:

ResNet101-CFBI+-DAVIS | ResNet101-DeepLabV3+ | **80.1** | **85.9** | **83.0** | **5.52** | [Click](https://drive.google.com/file/d/1KkU-Dxew1wI1affTjN1LmTld0kgpUUQH/view?usp=share_link)

ResNet101-CFBI-DAVIS | ResNet101-DeepLabV3+ | 79.3 | 84.5 | 81.9 | 5.88 | [Click](https://drive.google.com/file/d/1G6tVaaU4RYKXKKFxqxdE_p8Fwju_0nVE/view?usp=share_link) 

ResNet101-F16-CFBI-DAVIS | ResNet101-DeepLabV3+ | 79.2 | 84.4 | 81.8 | 7.38 (25.5%↑) | The same as above

ResNet101-Fast-CFBI-DAVIS | ResNet101-DeepLabV3+ | 77.0 | 82.7 | 79.9 | **10.18 (73.1%↑)** | The same as above

MobileNetV2-CFBI-DAVIS | MobileNetV2-DeepLabV3+ | 76.5 | 80.3 | 78.4 | 6.94 | [Click](https://drive.google.com/file/d/1CB_A-E6in27R-dVfl0hvny0MDW8WQ2Rd/view?usp=share_link)

MobileNetV2-Fast-CFBI-DAVIS | MobileNetV2-DeepLabV3+ | 75.2 | 78.9 | 77.1 | **13.22 (90.5%↑)** | The same as above

## Citing

```

@inproceedings{yang2020CFBI,

  title={Collaborative video object segmentation by foreground-background integration},

  author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},

  booktitle={European Conference on Computer Vision},

  pages={332--348},

  year={2020},

  organization={Springer}

}

@article{yang2020CFBIP,

  author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

  title={Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration}, 

  year={2021},

  volume={},

  number={},

  pages={1-1},

  doi={10.1109/TPAMI.2021.3081597}

}

```