CoDet Project Overview
CoDet is a cutting-edge project focusing on open-vocabulary object detection, a field within computer vision that aims to identify a wide range of objects in images using expansive vocabulary datasets. The project, titled "CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection," was introduced by researchers Chuofan Ma, Yi Jiang, Xin Wen, Zehuan Yuan, and Xiaojuan Qi in the 2023 NeurIPS conference.
Key Features
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Open-Vocabulary Detector: CoDet trains open-vocabulary detectors using vast image-text pairs sourced from the web, enhancing the ability to recognize diverse objects without being limited to specific categories.
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Co-Occurrence Alignment: Unlike traditional methods that depend on region-text similarity, CoDet employs a novel strategy by aligning image regions and textual words through co-occurrence patterns. This approach improves detection precision and adaptability.
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Superior Performance: The project demonstrates outstanding results in open-vocabulary object detection tasks, particularly on the LVIS dataset, which is known for its challenging scope and breadth.
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Modern Integration: CoDet is designed to work seamlessly with contemporary visual foundation models and is integrated with Roboflow. This integration automates image labeling for training smaller, fine-tuned models, further simplifying the training process for users.
Installation and Setup
CoDet requires setting up a specific environment, starting with Python 3.8 and integrating frameworks like PyTorch. It also utilizes additional components such as Detectron2 for enhanced object detection capabilities. The installation involves steps to clone the CoDet repository, install dependencies, and configure specific tools for optimal performance.
Data Preparation
For experimentation, CoDet utilizes a variety of datasets: LVIS and Conceptual Captions (CC3M) for LVIS experiments, COCO for COCO experiments, and Objects365 for cross-dataset evaluations. Users need to download these datasets and organize them properly within the CoDet's directory structure to proceed with data processing.
Model Zoo
CoDet provides pre-trained models and configuration files for various backbones like ResNet50 and Swin-B. These models serve as a foundation for running detections on different datasets like COCO and LVIS. The project offers links to download these model checkpoints for immediate use.
Running Inference
CoDet allows testing custom images or videos by executing specific scripts and setting parameters like model weights and vocabulary. It supports customizable vocabularies, enabling users to tailor the detection capability to specific needs or scenarios. The process also includes running pre-trained models for evaluation purposes, including cross-dataset evaluations.
Training Guidelines
The project includes detailed configurations for training, which rely on pre-trained model weights for initialization. It provides instructions and scripts to conduct training sessions on single-node configurations. Users should adjust the learning rate based on the available computational resources.
Acknowledgments and Licensing
CoDet builds upon and acknowledges prior works from projects like Detic and EVA. It is distributed under the Apache License 2.0, ensuring open access to code and modifications.
Citation
Researchers and practitioners using CoDet in their work are encouraged to cite the publication to acknowledge the contributions of the original authors.
This comprehensive approach to open-vocabulary object detection positions CoDet as a significant advancement in the field, offering both high performance and practical usability for a wide audience.