_Pretrained_weights_provided__EfficientDet__Scalab_

# 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](https://arxiv.org/abs/1911.09070) (*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](http://cocodataset.org/#download). 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](https://arxiv.org/abs/1911.09070). - Our implementation borrows some parts from [RetinaNet.Pytorch](https://github.com/yhenon/pytorch-retinanet) ## Citation @article{EfficientDetSignatrix, Author = {Signatrix GmbH}, Title = {A Pytorch Implementation of EfficientDet Object Detection}, Journal = {https://github.com/signatrix/efficientdet}, Year = {2020} }