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Dummy ResNet Wrapper

This is an example README for community projects/. We have provided detailed explanations for each field in the form of html comments, which are visible when you read the source of this README file. If you wish to submit your project to our main repository, then all the fields in this README are mandatory for others to understand what you have achieved in this implementation. For more details, read our contribution guide or approach us in Discussions.

Description

This project implements a dummy ResNet wrapper, which literally does nothing new but prints "hello world" during initialization.

Usage

Training commands

In MMDetection's root directory, run the following command to train the model:

python tools/train.py projects/example_project/configs/faster-rcnn_dummy-resnet_fpn_1x_coco.py

For multi-gpu training, run:

python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/example_project/configs/faster-rcnn_dummy-resnet_fpn_1x_coco.py

Testing commands

In MMDetection's root directory, run the following command to test the model:

python tools/test.py projects/example_project/configs/faster-rcnn_dummy-resnet_fpn_1x_coco.py ${CHECKPOINT_PATH}

Results

Method Backbone Pretrained Model Training set Test set #epoch box AP Download
Faster R-CNN dummy DummyResNet - COCO2017 Train COCO2017 Val 12 0.8853 model | log

Citation 

@article{Ren_2017,
   title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
   year={2017},
   month={Jun},
}

Checklist

  • Milestone 1: PR-ready, and acceptable to be one of the projects/.

    • Finish the code

    • Basic docstrings & proper citation

    • Test-time correctness

    • A full README

  • Milestone 2: Indicates a successful model implementation.

    • Training-time correctness

  • Milestone 3: Good to be a part of our core package!

    • Type hints and docstrings

    • Unit tests

    • Code polishing

    • Metafile.yml

  • Move your modules into the core package following the codebase's file hierarchy structure.

  • Refactor your modules into the core package following the codebase's file hierarchy structure.