Here is our pytorch implementation of the model described in the paper EfficientDet: Scalable and Efficient Object Detection paper (Note: We also provide pre-trained weights, which you could see at ./trained_models)

An example of our model's output.

Create a data folder under the repository,

cd {repo_root}
mkdir data

• COCO: Download the coco images and annotations from coco website. Make sure to put the files as the following structure:

  COCO
  ├── annotations
  │   ├── instances_train2017.json
  │   └── instances_val2017.json
  │── images
      ├── train2017
      └── val2017
  

With our code, you can:

• Train your model by running python train.py
• Evaluate mAP for COCO dataset by running python mAP_evaluation.py
• Test your model for COCO dataset by running python test_dataset.py --pretrained_model path/to/trained_model
• Test your model for video by running python test_video.py --pretrained_model path/to/trained_model --input path/to/input/file --output path/to/output/file

We trained our model by using 3 NVIDIA GTX 1080Ti. Below is mAP (mean average precision) for COCO val2017 dataset

Some predictions are shown below:

• python 3.6
• pytorch 1.2
• opencv (cv2)
• tensorboard
• tensorboardX (This library could be skipped if you do not use SummaryWriter)
• pycocotools
• efficientnet_pytorch

• Mingxing Tan, Ruoming Pang, Quoc V. Le. "EfficientDet: Scalable and Efficient Object Detection." EfficientDet.
• Our implementation borrows some parts from RetinaNet.Pytorch

@article{EfficientDetSignatrix,
    Author = {Signatrix GmbH},
    Title = {A Pytorch Implementation of EfficientDet Object Detection},
    Journal = {https://github.com/signatrix/efficientdet},
    Year = {2020}
}