(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH
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.
| Dataset | Classes | #Train images | #Validation images | |------------------------|:---------:|:-----------------------:|:----------------------------:| | COCO2017 | 80 | 118k | 5k |
Create a data folder under the repository,
cd {repo_root} mkdir data
COCO ├── annotations │ ├── instances_train2017.json │ └── instances_val2017.json │── images ├── train2017 └── val2017
With our code, you can:
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 |
Some predictions are shown below:
@article{EfficientDetSignatrix, Author = {Signatrix GmbH}, Title = {A Pytorch Implementation of EfficientDet Object Detection}, Journal = {https://github.com/signatrix/efficientdet}, Year = {2020} }