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# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
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# C extensions | ||
*.so | ||
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# Distribution / packaging | ||
.Python | ||
build/ | ||
develop-eggs/ | ||
dist/ | ||
downloads/ | ||
eggs/ | ||
.eggs/ | ||
lib/ | ||
lib64/ | ||
parts/ | ||
sdist/ | ||
var/ | ||
wheels/ | ||
*.egg-info/ | ||
.installed.cfg | ||
*.egg | ||
MANIFEST | ||
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# PyInstaller | ||
# Usually these files are written by a python script from a template | ||
# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
*.manifest | ||
*.spec | ||
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# Installer logs | ||
pip-log.txt | ||
pip-delete-this-directory.txt | ||
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# Unit test / coverage reports | ||
htmlcov/ | ||
.tox/ | ||
.coverage | ||
.coverage.* | ||
.cache | ||
nosetests.xml | ||
coverage.xml | ||
*.cover | ||
.hypothesis/ | ||
.pytest_cache/ | ||
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# Translations | ||
*.mo | ||
*.pot | ||
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# Django stuff: | ||
*.log | ||
local_settings.py | ||
db.sqlite3 | ||
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# Flask stuff: | ||
instance/ | ||
.webassets-cache | ||
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# Scrapy stuff: | ||
.scrapy | ||
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# Sphinx documentation | ||
docs/_build/ | ||
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# PyBuilder | ||
target/ | ||
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# Jupyter Notebook | ||
.ipynb_checkpoints | ||
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# pyenv | ||
.python-version | ||
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# celery beat schedule file | ||
celerybeat-schedule | ||
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# SageMath parsed files | ||
*.sage.py | ||
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# Environments | ||
.env | ||
.venv | ||
env/ | ||
venv/ | ||
ENV/ | ||
env.bak/ | ||
venv.bak/ | ||
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# Spyder project settings | ||
.spyderproject | ||
.spyproject | ||
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# Rope project settings | ||
.ropeproject | ||
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# mkdocs documentation | ||
/site | ||
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# mypy | ||
.mypy_cache/ | ||
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testing_dataset/* | ||
experiments |
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# Installation | ||
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This document contains detailed instructions for installing dependencies for PySOT. We recommand using the [install.sh](install.sh). The code is tested on an Ubuntu 16.04 system with Nvidia GPU (We recommand 1080TI / TITAN XP). | ||
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### Requirments | ||
* Conda with Python 3.7. | ||
* Nvidia GPU. | ||
* PyTorch 0.4.1 | ||
* yacs | ||
* pyyaml | ||
* matplotlib | ||
* tqdm | ||
* OpenCV | ||
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## Step-by-step instructions | ||
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#### Create environment and activate | ||
```bash | ||
conda create --name pysot python=3.7 | ||
conda activate pysot | ||
``` | ||
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#### Install numpy/pytorch/opencv | ||
``` | ||
conda install numpy | ||
conda install pytorch=0.4.1 torchvision cuda90 -c pytorch | ||
pip install opencv-python | ||
``` | ||
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#### Install other requirements | ||
``` | ||
pip install pyyaml yacs tqdm colorama matplotlib cython tensorboardX | ||
``` | ||
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#### Build extensions | ||
``` | ||
python setup.py build_ext --inplace | ||
``` | ||
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## Try with scripts | ||
``` | ||
bash install.sh /path/to/your/conda pysot | ||
``` | ||
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## Downloading dataset |
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# PySOT Model Zoo | ||
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## Introduction | ||
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This file documents a large collection of baselines trained with pysot. All configurations for these baselines are located in the `experiments` directory. The tables below provide results about inference. Links to the trained models as well as their output are provided. | ||
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## Visual Tracking Baselines | ||
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### Short-term Tracking | ||
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| <sub>Model</br>(arch+backbone+xcorr)</sub> | <sub>VOT2016</br> (EAO/A/R) </sub> | <sub>VOT2018</br> (EAO/A/R) </sub> | <sub>OTB2015</br> (AUC/Prec.) </sub> | <sub>VOT2018-LT</br>(F1)</sub> | <sub>Speed</br> (fps) </sub> | <sub>Download</sub> | | ||
|:----------------------------------------:|:-:|:------------------------:|:--------------------:|:----------------:|:--------------:|:------------:| | ||
| <sub>siamrpn_alex_dwxcorr</sub> | <sub>0.393/0.618/0.238</sub> | <sub>0.352/0.576/0.290</sub> | - | - | <sub>180</sub> | [link](https://drive.google.com/open?id=1t62x56Jl7baUzPTo0QrC4jJnwvPZm-2m) | | ||
| <sub>siamrpn_alex_dwxcorr_otb</sub> | | - | <sub>0.666/0.876</sub> | - | <sub>180</sub> | [link](https://drive.google.com/open?id=1gCpmR85Qno3C-naR3SLqRNpVfU7VJ2W0) | | ||
| <sub>siamrpn_r50_l234_dwxcorr</sub> | <sub>0.464/0.642/0.196</sub> | <sub>0.415/0.601/0.234</sub> | - | - | <sub>35</sub> | [link](https://drive.google.com/open?id=1Q4-1563iPwV6wSf_lBHDj5CPFiGSlEPG) | | ||
| <sub>siamrpn_r50_l234_dwxcorr_otb</sub> | | - | <sub>0.696/0.914</sub> | - | <sub>35</sub> | [link](https://drive.google.com/open?id=1Cx_oHu6o0gNeH7F9zZrgevfAGdyWC4D5) | | ||
| <sub>siamrpn_mobilev2_l234_dwxcorr</sub> | <sub>0.455/0.624/0.214</sub> | <sub>0.410/0.586/0.229</sub> | - | - | <sub>75</sub> | [link](https://drive.google.com/open?id=1JB94pZTvB1ZByU-qSJn4ZAIfjLWE5EBJ) | | ||
| <sub>siammask_r50_l3</sub> | | <sub>0.407/0.598/0.243</sub> | - | - | <sub>56</sub> | [link](https://drive.google.com/open?id=1YbPUQVTYw_slAvk_DchvRY-7B6rnSXP9) | | ||
| <sub>siamrpn_r50_l234_dwxcorr_lt</sub> | | - | - | <sub>0.629</sub> | <sub>20</sub> | [link](https://drive.google.com/open?id=1lOOTedwGLbGZ7MAbqJimIcET3ANJd29A) | | ||
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Note: | ||
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- speed tested on GTX-1080Ti | ||
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### Video Object Segmentation Baselines | ||
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## License | ||
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All models available for download through this document are licensed under the [Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/). | ||
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META_ARC: "siamrpn_r50_l234_dwxcorr" | ||
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BACKBONE: | ||
TYPE: "pysot.models.backbone.resnet_atrous.resnet50" | ||
LAYERS: [0, 1, 2, 3] | ||
CHANNELS: [1024] | ||
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ADJUST: | ||
ADJUST: true | ||
TYPE: "pysot.models.neck.neck.AdjustAllLayer" | ||
ADJUST_CHANNEL: [256] | ||
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RPN: | ||
TYPE: 'pysot.models.head.rpn.DepthwiseRPN' | ||
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MASK: | ||
MASK: True | ||
MASK_TYPE: 'pysot.models.head.mask.MaskCorr' | ||
REFINE: True | ||
REFINE_TYPE: 'pysot.models.head.mask.Refine' | ||
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ANCHOR: | ||
STRIDE: 8 | ||
RATIOS: [0.33, 0.5, 1, 2, 3] | ||
SCALES: [8] | ||
ANCHOR_NUM: 5 | ||
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TRACK: | ||
TYPE: 'pysot.tracker.siammask_tracker.SiamMaskTracker' | ||
PENALTY_K: 0.10 | ||
WINDOW_INFLUENCE: 0.41 | ||
LR: 0.32 | ||
EXEMPLAR_SIZE: 127 | ||
INSTANCE_SIZE: 255 | ||
BASE_SIZE: 8 | ||
CONTEXT_AMOUNT: 0.5 | ||
MASK_THERSHOLD: 0.15 |
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META_ARC: "siamrpn_alex_dwxcorr" | ||
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BACKBONE: | ||
TYPE: "pysot.models.backbone.alexnet.alexnet" | ||
LAYERS: [-1] | ||
CHANNELS: [256] | ||
WIDTH_MULT: 1. | ||
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ADJUST: | ||
ADJUST: False | ||
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RPN: | ||
TYPE: 'pysot.models.head.rpn.DepthwiseRPN' | ||
WEIGHTED: False | ||
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MASK: | ||
MASK: False | ||
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ANCHOR: | ||
STRIDE: 8 | ||
RATIOS: [0.33, 0.5, 1, 2, 3] | ||
SCALES: [8] | ||
ANCHOR_NUM: 5 | ||
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TRACK: | ||
TYPE: 'pysot.tracker.siamrpn_tracker.SiamRPNTracker' | ||
PENALTY_K: 0.16 | ||
WINDOW_INFLUENCE: 0.40 | ||
LR: 0.30 | ||
EXEMPLAR_SIZE: 127 | ||
INSTANCE_SIZE: 287 | ||
BASE_SIZE: 0 | ||
CONTEXT_AMOUNT: 0.5 |
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META_ARC: "siamrpn_mobilev2_l234_dwxcorr" | ||
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BACKBONE: | ||
TYPE: "pysot.models.backbone.mobile_v2.mobilenetv2" | ||
LAYERS: [3, 5, 7] | ||
WIDTH_MULT: 1.4 | ||
CHANNELS: [44, 134, 448] | ||
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ADJUST: | ||
ADJUST: true | ||
TYPE: "pysot.models.neck.neck.AdjustAllLayer" | ||
ADJUST_CHANNEL: [256, 256, 256] | ||
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RPN: | ||
TYPE: 'pysot.models.head.rpn.MultiRPN' | ||
WEIGHTED: False | ||
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MASK: | ||
MASK: False | ||
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ANCHOR: | ||
STRIDE: 8 | ||
RATIOS: [0.33, 0.5, 1, 2, 3] | ||
SCALES: [8] | ||
ANCHOR_NUM: 5 | ||
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TRACK: | ||
TYPE: 'pysot.tracker.siamrpn_tracker.SiamRPNTracker' | ||
PENALTY_K: 0.04 | ||
WINDOW_INFLUENCE: 0.4 | ||
LR: 0.5 | ||
EXEMPLAR_SIZE: 127 | ||
INSTANCE_SIZE: 255 | ||
BASE_SIZE: 8 | ||
CONTEXT_AMOUNT: 0.5 |
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META_ARC: "siamrpn_r50_l234_dwxcorr" | ||
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BACKBONE: | ||
TYPE: "pysot.models.backbone.resnet_atrous.resnet50" | ||
LAYERS: [2, 3, 4] | ||
CHANNELS: [512, 1024, 2048] | ||
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ADJUST: | ||
ADJUST: true | ||
TYPE: "pysot.models.neck.neck.AdjustAllLayer" | ||
ADJUST_CHANNEL: [256, 256, 256] | ||
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RPN: | ||
TYPE: 'pysot.models.head.rpn.MultiRPN' | ||
WEIGHTED: True | ||
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MASK: | ||
MASK: False | ||
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ANCHOR: | ||
STRIDE: 8 | ||
RATIOS: [0.33, 0.5, 1, 2, 3] | ||
SCALES: [8] | ||
ANCHOR_NUM: 5 | ||
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TRACK: | ||
TYPE: 'pysot.tracker.siamrpn_tracker.SiamRPNTracker' | ||
PENALTY_K: 0.05 | ||
WINDOW_INFLUENCE: 0.42 | ||
LR: 0.38 | ||
EXEMPLAR_SIZE: 127 | ||
INSTANCE_SIZE: 255 | ||
BASE_SIZE: 8 | ||
CONTEXT_AMOUNT: 0.5 |
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