If you’re venturing into the world of object detection, the CenterNet repository offers an elegant solution that can boost your performance while saving time. This guide will walk you through the setup and use of CenterNet, along with troubleshooting tips to keep your experience smooth.

Motivation Behind CenterNet

Inspired by the paper Objects as Points, CenterNet seeks to refine the object detection framework by simplifying the implementation. The objective is to achieve higher points while minimizing complexity. This repo improves upon its predecessors, offering a more streamlined approach to training models.

Performance Overview

One of the outstanding features of this repo is its ability to reduce training time while enhancing accuracy. Below is a performance table comparing different backbones:

   Backbone       mAP      FPS      V100 FPS   trained model
   :------------::-------::-------::---------::-----------------:
   ResNet-18      29.8     92       113        [google drive](https://drive.google.com/open?id=1D3tO95sdlsh9egOjOg0N-2HHmMfqbt5X)
   ResNet-50      34.9     57       71         [google drive](https://drive.google.com/open?id=1t5Bw520_fJrn3aeSVxDBYNIgwpNdLR5s)
   ResNet-101     36.8     43       50         [google drive](https://drive.google.com/open?id=1762Y93i9QreUTHq-87Ir73R2nNcrHuk0)

Key Features

• Configurability: Config files act like objects, allowing easy access to configurations during debugging.
• Streamlined Training: Invoke a single command to start training and testing processes.
• Automatic Reporting: Performance reports generated automatically post-training in markdown format.
• Optimized Operations: Vectorized operations improve speed and efficiency of training.

How to Install and Train Your Model

Requirements

• Python = 3.6
• PyTorch = 1.3
• torchvision matching your PyTorch version
• OpenCV
• pycocotools:
• Run in shell: pip install cython; pip install git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI
• GCC = 4.9:
• Run in shell: gcc --version

Installation Steps

To install CenterNet, make sure you have at least one GPU available:

pip install -e .

Training Your Model

For example, to train CenterNet with the ResNet-18 backbone, execute the following command:

cd playground
centernet.res18.coco.512size
dl_train --num-gpus 8

After training, a README.md file is generated automatically with model performance reports. Ensure your machine has over 150GB memory for training, and adjust the NUM_WORKER value in config.py if memory is insufficient.

Testing and Evaluation

To evaluate the downloaded model, use:

dl_test --num-gpus 8  MODEL.WEIGHTS path/to/your/save_dir/ckpt.pth

Troubleshooting Tips

If you encounter issues while running your models, consider the following:

• Ensure all dependencies are installed as per the requirements.
• Check GPU availability and configurations, especially memory limitations.
• Consult the Detectron2 GitHub for related inquiries and assistance.

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