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GitHub - signatrix/efficientdet: (Pretrained weights provided) EfficientDet: Sca...

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source link: https://github.com/signatrix/efficientdet
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README.md

EfficientDet: Scalable and Efficient Object Detection

Introduction

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)

video.gif
An example of our model's output.

Datasets

Dataset Classes #Train images #Validation images COCO2017 80 118k 5k

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

How to use our code

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

Experiments

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

Average Precision IoU=0.50:0.95 area= all maxDets=100 0.314 Average Precision IoU=0.50 area= all maxDets=100 0.461 Average Precision IoU=0.75 area= all maxDets=100 0.343 Average Precision IoU=0.50:0.95 area= small maxDets=100 0.093 Average Precision IoU=0.50:0.95 area= medium maxDets=100 0.358 Average Precision IoU=0.50:0.95 area= large maxDets=100 0.517 Average Recall IoU=0.50:0.95 area= all maxDets=1 0.268 Average Recall IoU=0.50:0.95 area= all maxDets=10 0.382 Average Recall IoU=0.50:0.95 area= all maxDets=100 0.403 Average Recall IoU=0.50:0.95 area= small maxDets=100 0.117 Average Recall IoU=0.50:0.95 area= medium maxDets=100 0.486 Average Recall IoU=0.50:0.95 area= large maxDets=100 0.625

Results

Some predictions are shown below:

1.jpg 2.jpg 3.jpg

4.jpg 5.jpg 6.jpg

7.jpg 8.jpg 9.jpg

Requirements

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

References

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

Citation

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

report

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