Awesome
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)
<p align="center"> <img src="demo/video.gif"><br/> <i>An example of our model's output.</i> </p>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:
<img src="demo/1.jpg" width="280"> <img src="demo/2.jpg" width="280"> <img src="demo/3.jpg" width="280">
<img src="demo/4.jpg" width="280"> <img src="demo/5.jpg" width="280"> <img src="demo/6.jpg" width="280">
<img src="demo/7.jpg" width="280"> <img src="demo/8.jpg" width="280"> <img src="demo/9.jpg" width="280">
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}
}