T-Rex Label

为什么选择 T-Rex Label ?

T-Rex Label 专为高效而生:无需微调,一步提示,开箱即用,让数据标注快人一步

T-Rex Label 内置开集检测模型,无需额外训练或微调即可一键检测,显著减少因预训练模型链路冗长而带来的时间成本。
无需微调,开箱即用的模型
T-Rex Label 内置开集检测模型,无需额外训练或微调即可一键检测,显著减少因预训练模型链路冗长而带来的时间成本。
只需框选目标,T-Rex Label 即可自动识别相似目标,并将提示信息应用到其他图像,实现一轮提示下的跨图批量标注。
一轮提示,批量标注的体验
只需框选目标,T-Rex Label 即可自动识别相似目标,并将提示信息应用到其他图像,实现一轮提示下的跨图批量标注。
T-Rex Label 是基于浏览器的标注工具,无需安装与部署,助力团队快速上手,显著降低使用和维护成本。
无需安装,快速上手的工具
T-Rex Label 是基于浏览器的标注工具,无需安装与部署,助力团队快速上手,显著降低使用和维护成本。

应用场景

T-Rex 具备卓越的零样本检测能力,无需微调即可直接赋能各行各业的复杂场景标注,为各类应用提供数据支持

在农业领域,T-Rex Label 借助其先进的数据集标注功能简化了作物监测流程。用户能够轻松地对植物和害虫的图像进行标注,以便高效追踪作物的生长模式,并主动识别病虫害之类的问题。

易于集成

T-Rex Label 兼容主流数据格式的导入与导出,无缝衔接视觉 AI 工作流

dino
Kaggle DatasetsKaggle Datasets
Roboflow UniverseRoboflow Universe
ModelScopeModelScope
Hugging FaceHugging Face
Label StudioLabel Studio
FiftyOneFiftyOne
PyTochPyToch
TensorFlowTensorFlow
KerasKeras
Roboflow TrainRoboflow Train

行业认可

T-Rex Label 已经帮助了数万名计算机视觉工程师加速标注流程,打造高质量数据集

T-Rex2 breaks free from the limitations of traditional models. It takes visual prompts as input. You can give input by drawing a bounding box around an object and it will detect the rest of the similar objects in that image. It also recognizes objects outside of its initial training set without retraining.

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Ahsan Raza
Software and CV Engineer

T-Rex2 is a powerhouse in the world of multi-modal AI, designed to excel in a variety of real-world applications. Whether you're in agriculture, industry, livestock monitoring, biology, medicine, OCR, retail, electronics, transportation, or logistics, T-Rex2 has got you covered!

M
Mayur Asodara
CV Engineer

I have to say I was very impressed by T-Rex2 model's performance. For context, I only annotated 3 objects(visual prompts) in each of the attached images and the model was able to detect the rest. Worked much better than text prompts for me.

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Eric Kimwatan
Data Scientist

T-Rex Label is so easy to use with zero-shot object detection ability, especially in detecting rare objects in large quantities. For my recent CV projects, I quickly finished annotation tasks with no hassle. Highly recommended!

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William Liu
Principal System Architect