Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The official implementation of CFBI(+): Collaborative Video Object Segmentation by (Multi-scale) Foreground-Background Integration.
| Date | Stars |
|---|---|
| 2026-07-24 | 321 |
| 2026-07-25 | 321 |
| 2026-07-28 | 321 |
| 2026-07-30 | 321 |
| 2026-08-06 | 321 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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: <[email protected]>. And please inform us of your institution and the purpose of using CFBI in the email. Thank you for your understanding!** Framework: <div align=center><img src="https://github.com/z-x-yang/CFBI/raw/master/utils/overview.png" width="80%"/></div> Some video segmentation results: <div align=center><img src="https://github.com/z-x-yang/CFBI/raw/master/utils/quality.png" width="80%"/></div> ## 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 infere
Excerpt of 8,244 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5d09af3dac69bad9, topic:computer-vision