# Zero-shot Object Detection

LLMDet is a powerful open-vocabulary object detector supervised by large language models, a highlight paper at CVPR2025

LLMDet is an open-vocabulary object detector supervised by large language models, capable of zero-shot object detection.

A zero-shot object detection model based on Qwen2.5-VL-3B-Instruct, enhanced with VLM-R1 reinforcement learning, supporting open vocabulary detection tasks.

A zero-shot object detection model based on the Swin-Tiny architecture, supporting French and English, suitable for various object detection tasks.

YOLOE is a real-time visual omni-model that supports various vision tasks including zero-shot object detection.

YOLOE is a real-time visual omni-model that combines object detection and visual understanding capabilities, suitable for various visual tasks.

YOLOE is a zero-shot object detection model capable of detecting various objects in visual scenes in real-time.

A vision-language model based on Qwen2.5-VL-3B-Instruct, enhanced with VLM-R1 reinforcement learning, focusing on referring expression comprehension tasks.

A lightweight zero-shot object detection model in ONNX format, compatible with Transformers.js, suitable for browser-side deployment.

OmDet-Turbo is an efficient fusion-head open-vocabulary detection model based on real-time Transformer, suitable for zero-shot object detection tasks.

OWLv2 is a vision-language pre-trained model focused on object detection and localization tasks.

OWLv2 is a zero-shot text-conditioned object detection model that can locate objects in images through text queries.

OWLv2 is a zero-shot text-conditioned object detection model that can locate objects in images through text queries.

OWLv2 is a zero-shot text-conditioned object detection model that can detect objects in images through text queries without requiring category-specific training data.

Grounding DINO is an open-set object detection model that achieves zero-shot object detection capabilities by combining the DINO detector with a text encoder.

OWL-ViT is a zero-shot text-conditioned object detection model that can retrieve objects in images through text queries.

OWL-ViT is a zero-shot text-conditioned object detection model that can detect objects in images via text queries.