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https://github.com/sydney0zq/PML
Re-implementation of "Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning"
https://github.com/sydney0zq/PML
Last synced: 11 days ago
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Re-implementation of "Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning"
- Host: GitHub
- URL: https://github.com/sydney0zq/PML
- Owner: sydney0zq
- Created: 2019-11-22T12:04:02.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-22T12:19:48.000Z (almost 5 years ago)
- Last Synced: 2024-08-02T05:23:01.685Z (3 months ago)
- Language: Python
- Size: 27.3 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.txt
Awesome Lists containing this project
- awesome-blazingly-fast - PML - Re-implementation of "Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning" (Python)
README
README.txt
:Author: qiang.zhou
:Email: [email protected]
:Date: 2018-10-11 21:46
Project description:
This project dedicates to reproduce CVPR 18 paper 'Blazingly Fast Video Object
Segmentation with Pixel-Wise Metric Learning'. Author Chen wants to firstly
embed frames into an embedding space, and then use metric learning to retrieve
foreground and background pixels under the guide of first frame and annotation,
which is a novel way to do Video Object Segmentation task.
This project tries to reproduce the results reported in his paper, but finally
has a gap about 0.8~1.5. However, I think it is enough to do further research.
================================================================
Deep learning:
1. Data preparation
DAIVS
+ trainval
+ Annotations
+ ImageSets
+ JPEGImages
+ testdev
+ testchallenge
You could download them from `http://davischallenge.org`.
2. Init model preparation
init_models/deeplabv2_voc.pth
Deeplab pretrained model is borrowed from
`https://github.com/speedinghzl/Pytorch-Deeplab`, download it by yourself.
Or download from: https://drive.google.com/open?id=19bHrNKQs4JzqZpoPSO5ntwMbqWQU8TIJ
3. Start to train
This project could train with single or multi GPU(s). You could choose one
depending on resources you own.
:Single GPU:
`CUDA_VISIBLE_DEVICES=0 python3 train.py --batch_size 4 \
--num_epochs 100 \
--learning_rate 2.5e-5 \
--alpha 0.7 \
--image_size 321 321 \
--gpus 0 \
--log_file ./experiments/run.log`
4. Evaluate on DAVIS 16 val dataset
As author Chen introduces `Bilater Solver`, which is a post-process for refine
upsampled masks, it locates in `PROJ_ROOT/net/bs.py`，and you could run test by:
`CUDA_VISIBLE_DEVICES=0 python3 infer_bs.py`
================================================================
Coda:
Author Chen doesnot open this project's source code, therefore I could not make sure
my implementation absoultely right.
The accuracy report in paper:
Spat.-Temp. Online Adapt. Mean J Mean F Mean J&F
72.0 73.6 72.8
√ 73.2 75.0 74.1
√ 74.3 78.1 76.2
√ √ 75.5 79.3 77.4
---
My implemetation(Stable result):
Spat.-Temp. Online Adapt. Mean J Mean F Mean J&F
√ 73.5
Thanks:
Many thanks to https://github.com/braindeadpool/bf-vos.