EfficientDet-Pytorch

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

It is based on the

• the paper EfficientDet: Scalable and Efficient Object Detection
• official TensorFlow implementation
• Pytorch implementation of EfficientNet

Models

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.