dynperceiverbaseddetection
2024 Spring KAIST CS470 Project - Team 10
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
·
Repository
2024 Spring KAIST CS470 Project - Team 10
Basic Info
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 1
- Releases: 0
Created about 2 years ago
· Last pushed about 2 years ago
Metadata Files
Readme
Contributing
License
Code of conduct
Citation
README.md
[2024 Spring] CS470 Team 10
The results are on paper.
Forked from https://github.com/open-mmlab/mmdetection
Original mmdetection README
OpenMMLab website
HOT
OpenMMLab platform
TRY IT OUT
English | [简体中文](README_zh-CN.md)
Major features
- **Modular Design** We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. - **Support of multiple tasks out of box** The toolbox directly supports multiple detection tasks such as **object detection**, **instance segmentation**, **panoptic segmentation**, and **semi-supervised object detection**. - **High efficiency** All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). - **State of the art** The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. The newly released [RTMDet](configs/rtmdet) also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.
- [Train & Test](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#train-test)
- [Learn about Configs](https://mmdetection.readthedocs.io/en/latest/user_guides/config.html)
- [Inference with existing models](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html)
- [Dataset Prepare](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Test existing models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/test.html)
- [Train predefined models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html)
- [Train with customized datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html#train-with-customized-datasets)
- [Train with customized models and standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/new_model.html)
- [Finetuning Models](https://mmdetection.readthedocs.io/en/latest/user_guides/finetune.html)
- [Test Results Submission](https://mmdetection.readthedocs.io/en/latest/user_guides/test_results_submission.html)
- [Weight initialization](https://mmdetection.readthedocs.io/en/latest/user_guides/init_cfg.html)
- [Use a single stage detector as RPN](https://mmdetection.readthedocs.io/en/latest/user_guides/single_stage_as_rpn.html)
- [Semi-supervised Object Detection](https://mmdetection.readthedocs.io/en/latest/user_guides/semi_det.html)
- [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
- Advanced Guides
- [Basic Concepts](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)
- [Component Customization](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)
- [How to](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#how-to)
We also provide object detection colab tutorial [](demo/MMDet_Tutorial.ipynb) and instance segmentation colab tutorial [](demo/MMDet_InstanceSeg_Tutorial.ipynb).
To migrate from MMDetection 2.x, please refer to [migration](https://mmdetection.readthedocs.io/en/latest/migration.html).
## Overview of Benchmark and Model Zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
Architectures
Components
| Backbones | Necks | Loss | Common |
|
Owner
- Name: CS470 Team 10
- Login: cs470-team10
- Kind: organization
- Location: Korea, South
- Repositories: 1
- Profile: https://github.com/cs470-team10
CS470 Team 10
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMDetection Contributors" title: "OpenMMLab Detection Toolbox and Benchmark" date-released: 2018-08-22 url: "https://github.com/open-mmlab/mmdetection" license: Apache-2.0
GitHub Events
Total
Last Year
Dependencies
.github/workflows/deploy.yml
actions
- actions/checkout v2 composite
- actions/setup-python v2 composite
.circleci/docker/Dockerfile
docker
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/Dockerfile
docker
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile
docker
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile
docker
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt
pypi
- albumentations >=0.3.2
requirements/build.txt
pypi
- cython *
- numpy *
requirements/docs.txt
pypi
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- sphinx_rtd_theme ==0.5.2
- urllib3 <2.0.0
requirements/mminstall.txt
pypi
- mmcv >=2.0.0rc4,<2.2.0
- mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt
pypi
- fairscale *
- jsonlines *
- nltk *
- pycocoevalcap *
- transformers *
requirements/optional.txt
pypi
- cityscapesscripts *
- emoji *
- fairscale *
- imagecorruptions *
- scikit-learn *
requirements/readthedocs.txt
pypi
- mmcv >=2.0.0rc4,<2.2.0
- mmengine >=0.7.1,<1.0.0
- scipy *
- torch *
- torchvision *
- urllib3 <2.0.0
requirements/runtime.txt
pypi
- matplotlib *
- numpy *
- pycocotools *
- scipy *
- shapely *
- six *
- terminaltables *
- tqdm *
requirements/tests.txt
pypi
- asynctest * test
- cityscapesscripts * test
- codecov * test
- flake8 * test
- imagecorruptions * test
- instaboostfast * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- memory_profiler * test
- nltk * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- parameterized * test
- prettytable * test
- protobuf <=3.20.1 test
- psutil * test
- pytest * test
- transformers * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test
requirements/tracking.txt
pypi
- mmpretrain *
- motmetrics *
- numpy <1.24.0
- scikit-learn *
- seaborn *
requirements.txt
pypi
setup.py
pypi