OmDet-Turbo

[Paper 📄] [Model 🗂️]

Fast and accurate open-vocabulary end-to-end object detection

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🗓️ Updates

• 06/24/2024: Guidance for converting OmDet-Turbo to ONNX
• 03/25/2024: Inference code and a pretrained OmDet-Turbo-Tiny model released.
• 03/12/2024: Github open-source project created

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🔗 Related Works

If you are interested in our research, we welcome you to explore our other wonderful projects.

🔆 How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection(AAAI24)  🏠Github Repository

🔆 OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network(IET Computer Vision)

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📖 Introduction

This repository is the official PyTorch implementation for OmDet-Turbo, a fast transformer-based open-vocabulary object detection model.

⭐️Highlights

1. OmDet-Turbo is a transformer-based real-time open-vocabulary detector that combines strong OVD capabilities with fast inference speed. This model addresses the challenges of efficient detection in open-vocabulary scenarios while maintaining high detection performance.
2. We introduce the Efficient Fusion Head, a swift multimodal fusion module designed to alleviate the computational burden on the encoder and reduce the time consumption of the head with ROI.
3. OmDet-Turbo-Base model, achieves state-of-the-art zero-shot performance on the ODinW and OVDEval datasets, with AP scores of 30.1 and 26.86, respectively.
4. The inference speed of OmDetTurbo-Base on the COCO val2017 dataset reach 100.2 FPS on an A100 GPU.

For more details, check out our paper Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head

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⚡️ Inference Speed

Comparison of inference speeds for each component in tiny-size model.

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🛠️ How To Install

Follow the Installation Instructions to set up the environments for OmDet-Turbo

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🚀 How To Run

1. Download our pretrained model and the CLIP checkpoints.
2. Create a folder named resources, put downloaded models into this folder.
3. Run run_demo.py, the images with predicted results will be saved at ./outputs folder.

We already added language cache while inferring with run_demo.py. For more details, please open and check run_demo.py scripts.

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⚙️ How To Export ONNX Model

1. Replace OmDetV2Turbo in OmDet-Turbo_tiny_SWIN_T.yaml with OmDetV2TurboInfer
2. Run export.py, and the omdet.onnx will be exported.

In the above example, post processing is not included in onnx model , and all input size are fixed. You can add more post processing and change the input size according to your needs.

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📦 Model Zoo

The performance of COCO and LVIS are evaluated under zero-shot setting.

Model	Backbone	Pre-Train Data	COCO	LVIS	FPS (pytorch/trt)	Weight
OmDet-Turbo-Tiny	Swin-T	O365,GoldG	42.5	30.3	21.5/140.0	weight

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📝 Main Results

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Citation

Please consider citing our papers if you use our projects:

@article{zhao2024real,
  title={Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head},
  author={Zhao, Tiancheng and Liu, Peng and He, Xuan and Zhang, Lu and Lee, Kyusong},
  journal={arXiv preprint arXiv:2403.06892},
  year={2024}
}

@article{zhao2024omdet,
  title={OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network},
  author={Zhao, Tiancheng and Liu, Peng and Lee, Kyusong},
  journal={IET Computer Vision},
  year={2024},
  publisher={Wiley Online Library}
}