SSD: Single Shot MultiBox Object Detector in PyTorch
The SSD or Single Shot MultiBox Detector is an advanced method for object detection, implemented in PyTorch. This project is based on the 2016 paper by Wei Liu and collaborators, and it aims to provide a comprehensive tool for detecting objects in images using a single network.
Installation
To get started with this project, it's essential to have PyTorch installed. Once PyTorch is set up according to your environment, cloning the repository is the next step. It's important to note that this project supports Python 3 and above only. The dataset required for training and evaluation can be obtained by following straightforward instructions. Additionally, the project offers real-time training loss visualization using Visdom, which can be set up through a few simple commands.
Datasets
The SSD.pytorch project is compatible with popular datasets like COCO and PASCAL VOC, and the team plans to extend support to ImageNet soon. It simplifies the download and setup process through provided bash scripts and includes dataset loaders that integrate seamlessly with PyTorch's dataset API.
COCO Dataset
COCO (Common Objects in Context) is one of the supported datasets. Users can download the COCO 2014 dataset using a provided script, which simplifies the setup.
VOC Dataset
For the VOC (Visual Object Classes) dataset, there are separate scripts provided for downloading VOC2007 and VOC2012 datasets. These scripts allow users to prepare the datasets quickly for use with SSD.pytorch.
Training SSD
Training the SSD model involves a few steps. Initially, users need to download the VGG-16 base network weights, which act as a foundation for the SSD model. The project provides a train script where users can specify training parameters, either as script flags or by modifying the script manually. It's advised to use an NVIDIA GPU for efficient training, and Visdom can be used to visualize training progress if needed.
Evaluation
To assess the performance of a trained model, users can employ the evaluation script provided. Similar to training, parameters for evaluation can be adjusted as per individual needs by either flagging them or editing them directly within the script.
Performance
The project demonstrates impressive performance metrics, specifically on the VOC2007 test dataset. For instance, mean Average Precision (mAP) scores are documented with various training configurations, showing how the model fares under different conditions. Also, it highlights the model's Frames Per Second (FPS) capability on a GTX 1060, which clocks in at approximately 45.45 FPS.
Demos
SSD.pytorch also includes exciting demo applications showcasing the model's capabilities:
Pre-trained Models
The project offers pre-trained SSD models that can be directly used for object detection tasks. These models are trained on different datasets, and users can download the corresponding PyTorch state dictionaries.
Demo Notebook
A Jupyter notebook is available for users to run SSD demonstrations interactively. It's user-friendly and does not require extensive setup beyond having Jupyter installed.
Webcam Demo
For a more dynamic experience, there's a webcam demo that allows real-time object detection using either CPU or GPU. This demo requires OpenCV installed with Python bindings and can utilize multi-threading for enhanced performance.
Future Work
The project continues to evolve, with plans to support SSD512, add new datasets, and extend capabilities for custom datasets.
Authors and Contributions
The project was developed by Max deGroot and Ellis Brown. They express gratitude to the community for contributions and support, despite the project being a side endeavor.
References
The SSD model is based on the original paper by Wei Liu and others. It also draws inspiration from other implementations in Caffe, Chainer, Keras, MXNet, and TensorFlow, highlighting its widespread influence and utility in the field of object detection.