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All Projects → lucifer443 → Spinenet Pytorch

lucifer443 / Spinenet Pytorch

Licence: apache-2.0
This project is a kind of implementation of SpineNet(CVPR 2020) using mmdetection.

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SpineNet-Pytorch

SpineNet is a CVPR 2020 paper for object detection. This project is a kind of implementation of SpineNet using mmdetection.

It is based on the

Models

Variant mAP Params FLOPs mAP in paper Params in paper FLOPs in paper
SpineNet-49S 39.1 11.15M 30.04B 39.9 12.0M 33.8B
SpineNet-49 42.7 28.31M 83.7B 42.8 28.5M 85.4B
SpineNet-96 —— 42.74M 261.35B 47.1 43.0M 265.4B
SpineNet-143 —— —— —— 48.1 66.9M 524.4B
SpineNet-190 —— —— —— —— 163.6M 1885B

Note: The parameters and flops are a little different from paper, so I think there are some difference between my code and official's code. More information about models can see in MODEL_DETAILS.md

Usage

  1. Install mmdetection

    This implementation is based on mmdetection(v1.1.0+8732ed9). Please refer to INSTALL.md for installation and dataset preparation.

  2. Copy the codes to mmdetection directory

    cp -r mmdet/ ${MMDETECTION_PATH}/
cp -r configs/ ${MMDETECTION_PATH}/

  3. Prepare data

    The directories should be arranged like this:

    >   mmdetection
>     ├── mmdet
>     ├── tools
>     ├── configs
>     ├── data
>     │   ├── coco
>     │   │   ├── annotations
>     │   │   ├── train2017
>     │   │   ├── val2017
>     │   │   ├── test2017

  4. Train D0 with 4 GPUs

    CONFIG_FILE=configs/spinenet/spinenet_49_B_8gpu.py
./ tools/dist_train.py ${CONFIG_FILE} 4

  5. Calculate parameters and flops

     python tools/get_flops.py ${CONFIG_FILE} --shape $SIZE $SIZE

  6. Test

    python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --out  ${OUTPUT_FILE} --eval bbox

More usages can reference mmdetection documentation.

Update log

  • [2020-05-30] Add SpineNet-49S results.
  • [2020-05-13] Update codes and results.
  • [2020-05-02] Create this repository.
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