Introduction to FastReID
FastReID is an advanced research platform tailored for implementing state-of-the-art re-identification algorithms. It stands as a comprehensive rewrite of its predecessor, known as the "reid strong baseline". The project focuses on boosting the performance and efficiency of re-identification tasks, which involve recognizing individuals across different images or video frames.
Key Updates
FastReID has gone through numerous updates to improve its functionality and feature set:
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September 2021: The DG-ReID, an advanced re-id algorithm, was updated, enhancing the system's capability. Interested users can delve into their research paper for extensive details.
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June 2021: Support for contiguous parameters was added. This update resulted in a performance acceleration of approximately 20%, making operations faster and more efficient.
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May 2021: The feature set was expanded with the support for Vision Transformer backbones. This addition allows for more robust and versatile model configurations like those in the
bagtricks_vit.ymlfor Market1501 dataset. -
April 2021: Partial FC support was introduced through the FastFace project, extending the platform's application scope.
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January 2021: The release of TRT network definition APIs within the FastRT project, contributed by Darren, marked a significant milestone. Also, the NAIC20 re-id track showcased its first-place solution using FastReID's capabilities.
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October 2020: FastReID integrated Hyper-Parameter Optimization for better tuning and performance through the FastTune project.
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August 2020: With the help of guan'an wang, model distillation support was introduced, and ONNX/TensorRT converter capabilities were added to facilitate model deployment across different setups.
Core Features
FastReID is packed with features designed to enhance re-identification tasks:
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It can handle various re-id scenarios such as cross-domain and partial re-id, and is optimized for performance with distributed training support across multiple GPUs.
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A diverse array of evaluation metrics and visualization methods are provided, supported by an array of state-of-the-art results.
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It functions as a library to support numerous projects, enabling the sharing and development of open-source research projects.
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The dependency on the high-level library Ignite has been removed, with the project being powered by PyTorch, ensuring a more streamlined and efficient framework.
Getting Started
FastReID has a structured architecture, guided by the PyTorch-Project-Template, making it easy for users to understand and navigate through the framework. The documentation is detailed, providing ample resources for learners and practitioners to get familiar and start utilizing the platform effectively.
Model Zoo and Deployment
FastReID offers a Model Zoo with a vast collection of baseline results and trained models, available for download. For developers looking to deploy models in various formats like Caffe, ONNX, and TensorRT, the project provides scripts and examples to facilitate these transitions efficiently.
Library and Licensing
FastReID is released under the Apache 2.0 license, promoting free use and distribution of the software within this permissive framework. Scholars and developers are encouraged to cite FastReID in their research to support the growing community and recognize the efforts of the developers.
In summary, FastReID reveals itself as a powerful and indispensable tool for re-identification research, promising accelerated performance, flexibility, and a broad scope of applications in the swiftly evolving field of artificial intelligence and machine learning.