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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Real-time and accurate open-vocabulary end-to-end object detection
| Date | Stars |
|---|---|
| 2026-07-30 | 1390 |
| 2026-08-10 | 1392 |
| 2026-08-18 | 1393 |
| 2026-09-06 | 1393 |
| 2026-09-09 | 1395 |
| 2026-09-10 | 1397 |
| 2026-09-14 | 1398 |
| 2026-09-15 | 1398 |
| 2026-09-16 | 1399 |
| 2026-09-17 | 1406 |
| 2026-09-30 | 1406 |
| 2026-10-06 | 1407 |
| 2026-10-12 | 1408 |
| 2026-10-23 | 1409 |
| 2026-11-04 | 1410 |
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# OmDet-Turbo <p align="center"> <a href="https://arxiv.org/abs/2403.06892"><strong> [Paper 📄] </strong></a> <a href="https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T"><strong> [HuggingFace Model 🗂️] </strong></a> <a href="https://modelscope.cn/models/Om_AI_Lab/omdet-turbo-swin-tiny-hf"><strong> [ModelScope Model 🗂️] </strong></a> </p> <p align="center"> Fast and accurate open-vocabulary end-to-end object detection </p> *** ## 🗓️ Updates * 03/12/2026：We are excited to announce that our models are now available on [ModelScope](https://modelscope.cn/models/Om_AI_Lab/omdet-turbo-swin-tiny-hf)! * 09/26/2024：OmDet-Turbo has been integrated into Transformers version 4.45.0. The code is available at [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/omdet_turbo), and the Hugging Face model is available at [here](https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf). * 07/05/2024: Our new open-source project, [OmAget: A multimodal agent framework for solving complex tasks](https://github.com/om-ai-lab/OmAgent) is available !!! Additionally, OmDet has been seamlessly integrated as an OVD tool within it. Feel free to delve into our innovative multimodal agent framework. * 06/24/2024: Guidance for [converting OmDet-Turbo to ONNX](https://github.com/om-ai-lab/OmDet#:~:text=How%20To%20Export%20ONNX%20Model) * 03/25/2024: Inference code and a pretrained OmDet-Turbo-Tiny model released. * 03/12/2024: Github open-source project created *** ## 🔗 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](https://arxiv.org/abs/2308.13177)(AAAI24) 🏠[Github Repository](https://github.com/om-ai-lab/OVDEval/tree/main) 🔆 [OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cvi2.12268)(IET Computer Vision) *** ## 📖 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](https://arxiv.org/abs/2403.06892)** <img src="docs/turbo_model.jpeg" alt="model_structure" width="100%"> *** ## ⚡️ Inference Speed Comparison of inference speeds for each component in tiny-size model. <img src="docs/speed_compare.jpeg" alt="speed" width="100%"> *** ## 🛠️ How To Install Follow the [Installation Instructions](install.md) to set up the environments for OmDet-Turbo *** ## 🚀 How To Run ### Local Inference 1. Download our pretrained model and the [CLIP](https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/resolve/main/ViT-B-16.pt?download=true) 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. ### Run as a API Server 1. Download our pretrained model and the [CLIP](https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/resolve/main/ViT-B-16.pt?do
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c74a981328850771, topic:computer-vision, topic:object-detection, readme:computer vision
