If you’re delving into the exciting realm of object detection, you’re likely to come across EfficientDet, a remarkable model that balances efficiency and accuracy. In this guide, we’ll take a journey through ensuring you can implement EfficientDet in PyTorch with ease. Buckle up as we explore recent updates, installation procedures, datasets, training, evaluation, and troubleshooting!

Table of Contents

• Recent Update
• Benchmarking
• Installation
• Prerequisites
• Datasets
• Training EfficientDet
• Evaluation
• Performance
• Demo
• Future Work
• References

Recent Updates

EfficientDet has been consistently improved! Here are some pivotal enhancements:

• 06/01/2020: Added support for both DistributedDataParallel and DataParallel, enhanced augmentations, and improved evaluation.
• 12/17/2019: Features like Fast Normalized Fusion and modifications to RetinaHeads have been implemented.
• 07/12/2019: Initial support for EfficientDet-D0 through EfficientDet-D7 models added.

Benchmarking

We rigorously benchmark our code across three datasets: PASCAL VOC and COCO, utilizing various architectures from the EfficientNet family. For example, here’s how EfficientDet-D0 performs:

• PASCAL VOC 2007 (Train/Test: 07trainval/07test, scale=600, ROI Align):
• mAP for EfficientDet-D0 (with Weight): 62.16

Installation

Getting EfficientDet up and running in your environment is straightforward. Follow the steps below:

• Install PyTorch by selecting your environment and executing the appropriate command from the website.
• Clone the EfficientDet repository and install the required packages.
• Download the datasets based on the guidelines shared below.
• Note: Currently, we support PASCAL VOC and COCO datasets for training, and we plan to integrate ImageNet soon.

Prerequisites

Before diving in, ensure you have the following:

• Python 3.6+
• PyTorch 1.3+
• Torchvision 0.4.0+ (required for non-maximum suppression)
• Refer to requirements.txt for additional dependencies.

Datasets

To simplify dataset handling, we provide bash scripts for download and setup:

• PASCAL VOC: To download VOC2007 + VOC2012, run:

sh datasets/scripts/VOC2007.sh

sh datasets/scripts/VOC2012.sh

• Microsoft COCO: For COCO 2017, execute:

sh datasets/scripts/COCO2017.sh

Training EfficientDet

Training EfficientDet is straightforward. Use the following command structure:

python train.py --network efficientdet-d0

Here are a few examples for different datasets:

• With PASCAL VOC:

  python train.py --dataset VOC --dataset_root root/data/VOCdevkit --network efficientdet-d0 --batch_size 32

• With COCO:

  python train.py --dataset COCO --dataset_root ~/data/coco --network efficientdet-d0 --batch_size 32

Evaluation

To evaluate your trained network, use the following commands:

• With PASCAL VOC:

  python eval_voc.py --dataset_root ~data/VOCdevkit --weight .checkpoint_VOC_efficientdet-d0_261.pth

• With COCO: Instructions coming soon!

Performance

EfficientDet has shown remarkable efficiency and accuracy, as illustrated in the following comparison graphic:

Demo

To see EfficientDet in action, utilize the demo feature by running:

python demo.py --threshold 0.5 --iou_threshold 0.5 --score --weight checkpoint_VOC_efficientdet-d1_34.pth --file_name demo.png

Additionally, you can use your webcam for real-time demonstrations by executing:

python demo.py --threshold 0.5 --iou_threshold 0.5 --cam --score --weight checkpoint_VOC_efficientdet-d1_34.pth

Future Work

We’re constantly innovating! Here’s a glimpse into our future plans:

• Soft-NMS (pending)
• Model zoo (pending)
• Mobility enhancements (pending)
• C++ ONNX compatibility (pending)

References

For deep dives into the workings of EfficientDet:

• EfficientDet: Scalable and Efficient Object Detection
• Check out other implementations and inspirations including:
  • EfficientNet
  • SSD.PyTorch
  • mmdetection
  • RetinaNet.Pytorch
  • NMS.Torchvision

Troubleshooting

Running into issues? Here are some common solutions:

• Ensure all packages are installed correctly. Missing dependencies can lead to runtime errors.
• Double-check your dataset paths. If the program can’t find your dataset, it will fail to load.
• If you’re experiencing slow training times, consider using a more powerful GPU or implementing batch sizes that better fit your hardware.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy coding, and may your EfficientDet journey be fruitful!