Dummy ResNet Wrapper

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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

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

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  • Basic docstrings & proper citation

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  • Test-time correctness

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  • A full README

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• Milestone 2: Indicates a successful model implementation.

  • Training-time correctness

    <!-- If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range. -->

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

  • Type hints and docstrings

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  • Code polishing

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  • Metafile.yml

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