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EfficientDet-Pytorch

This project is a kind of implementation of EfficientDet using mmdetection.

It is based on the

Models

Variant mAP(val2017) Params FLOPs mAP(val2017) in paper Params in paper FLOPs in paper
D0 32.02 3.87M 2.55B 33.5 3.9M 2.5B
D1 37.78 6.62M 6.12B 39.1 6.6M 6.1B
D2 —— 8.09M 11B 42.5 8.1M 11B
D3 —— 12.02M 24.88B 45.9 12M 25B
D4 —— 20.7M 55.13B 49.0 21M 55B
D5 —— 33.63M 135.31B 50.5 34M 135B
D6 —— —— —— 51.3 52M 226B

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/efficientdet/efficientdet_d0_4gpu.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

  • [2020-04-27] Update results and add SyncBN in backbone.
  • [2020-04-20] Fix some bug in bifpn and use separate BN in head.
  • [2020-04-17] Add efficientdet-d0 training config.
  • [2020-04-16] Add efficientnet.py and retina_sepconv_head.py.
  • [2020-04-06] Create this repository.

Notice

  1. For small reason, I can't release the model. But you can reproduce the result easily using the config file that I provide.
  2. I find the training procedure of EfficientDet is unstable and there is a small chance that results can be 3% mAP lower.
  3. The number of bifpn in the latest version of paper is a little different from the first version, but the parameters and flops are the same. I use the structure in the latest version of paper.
  4. Training from scratch is a time-consuming task. For exmaple, it took me 4 days to train D0 from scratch using 4 GTX TiTAN V GPUs.