# EfficientDet

> [**EfficientDet: Scalable and Efficient Object Detection**](https://arxiv.org/pdf/1911.09070.pdf),
> Mingxing Tan, Ruoming Pang, Quoc V. Le,
> *CVPR 2020*

## Abstract

This is an implementation of [EfficientDet](https://github.com/google/automl) based on [MMDetection](https://github.com/open-mmlab/mmdetection/tree/main), [MMCV](https://github.com/open-mmlab/mmcv), and [MMEngine](https://github.com/open-mmlab/mmengine).
<br>
EfficientDet a new family of object detectors, which consistently achieve much better efficiency than prior art across a wide
spectrum of resource constraints.
In particular, with single model and single-scale, EfficientDet-D7 achieves stateof-the-art 55.1 AP on COCO test-dev with 77M parameters and 410B FLOP.
<br>
BiFPN is a simple yet highly effective weighted bi-directional feature pyramid network, which introduces learnable weights to learn the importance of different input features, while repeatedly applying topdown and bottom-up multi-scale feature fusion.
<br>
In contrast to other feature pyramid network, such as FPN, FPN + PAN, NAS-FPN, BiFPN achieves  the best accuracy with fewer parameters and FLOPs.

<div align="center">
<img src="https://github.com/zwhus/pictures/raw/main/Screenshot%20from%202023-01-31%2010-38-51.png">
</div>

## Usage

## Official TensorFlow Model

This project also supports [official tensorflow model](https://github.com/google/automl), it uses 90 categories and yxyx box encoding in training. If you want to use the original model weight to get official results, please refer to the following steps.

### Model conversion

Firstly, download EfficientDet [weights](https://github.com/google/automl/tree/master/efficientdet) and unzip,  please use the following command

```bash
tar -xzvf {EFFICIENTDET_WEIGHT}
```

Then, install tensorflow, please use the following command

```bash
pip install tensorflow-gpu==2.6.0
```

Lastly, convert weights from tensorflow to pytorch, please use the following command

```bash
python projects/EfficientDet/convert_tf_to_pt.py --backbone {BACKBONE_NAME} --tensorflow_weight {TENSORFLOW_WEIGHT_PATH} --out_weight {OUT_PATH}
```

### Testing commands

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

```bash
python tools/test.py projects/EfficientDet/configs/tensorflow/efficientdet_effb0_bifpn_8xb16-crop512-300e_coco_tf.py ${CHECKPOINT_PATH}
```

## Reproduce Model

For convenience, we recommend the current implementation version, it uses 80 categories and xyxy encoding in training. On this basis, a higher result was finally achieved.

### Training commands

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

```bash
python tools/train.py projects/EfficientDet/configs/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco.py
```

### Testing commands

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

```bash
python tools/test.py projects/EfficientDet/configs/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco.py ${CHECKPOINT_PATH}
```

## Results

Based on mmdetection, this project aligns the accuracy of the [official model](https://github.com/google/automl).

|                                                        Method                                                        |    Backbone     | Pretrained Model |  Training set  |   Test set   | Epoch | Val Box AP | Official AP |                                                                                                                                                                                                Download                                                                                                                                                                                                |
| :------------------------------------------------------------------------------------------------------------------: | :-------------: | :--------------: | :------------: | :----------: | :---: | :--------: | :---------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [efficientdet-d0\*](projects/EfficientDet/configs/tensorflow/efficientdet_effb0_bifpn_8xb16-crop512-300e_coco_tf.py) | efficientnet-b0 |     ImageNet     | COCO2017 Train | COCO2017 Val |  300  |    34.4    |    34.3     |                                                                                                                                                                                                                                                                                                                                                                                                        |
|         [efficientdet-d3](projects/EfficientDet/configs/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco.py)         | efficientnet-b3 |     ImageNet     | COCO2017 Train | COCO2017 Val |  300  |    47.2    |    46.8     | [model](https://download.openmmlab.com/mmdetection/v3.0/efficientdet/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco_20230223_122457-e6f7a833.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/efficientdet/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco_20230223_122457.log.json) |

**Note**:
\*means use [official tensorflow model](https://github.com/google/automl) weights to test.

## Citation

```BibTeX
@inproceedings{tan2020efficientdet,
  title={Efficientdet: Scalable and efficient object detection},
  author={Tan, Mingxing and Pang, Ruoming and Le, Quoc V},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={10781--10790},
  year={2020}
}
```

## Checklist

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OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
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- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.

  - [x] Finish the code

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- [ ] Milestone 3: Good to be a part of our core package!

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  - [ ] Code polishing

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