Tencent/WeVisOne-VL

选用指南

AI 根据 README 整理 · 2026/10/27 20:32:20

使用方式

通过Python库调用，支持transformers和vllm后端，提供Gradio演示。

查看 README 依据

### Installation ```bash git clone https://github.com/Tencent/WeVisOne-VL.git && cd WeVisOne-VL conda create -n wevisone-vl python=3.10 -y && conda activate wevisone-vl pip install -r requirements.txt && pip install -v -e . ``` ### Inference ```python from PIL import Image from wevisone_vl import WeVisOneVL from wevisone_vl.visualization import save_result model = WeVisOneVL("tencent/WeVisOne-VL-4B", backend="transformers") # or backend="vllm" # model = WeVisOneVL("tencent/WeVisOne-VL-2B", backend="transformers") # Use 2B instead # Object-level: open-vocabulary detection

| 📦 [Object-level pipeline](tutorials/01_object_level_pipeline.ipynb) | Detection, Referring, Counting, Pointing | | 🔤 [OCR pipeline](tutorials/02_ocr_pipeline.ipynb) | OCR, Text Grounding | | 🎭 [Pixel-level pipeline](tutorials/03_segmentation_matting_pipeline.ipynb) | Segmentation, Matting | ## 🎮 Gradio Demo Try the hosted demo: [2B](https://huggingface.co/spaces/hugging-apps/wevisone-vl-2b) / [4B](https://huggingface.co/spaces/hugging-apps/wevisone-vl-4b) Launch the demo locally: ```bash

部署要求

需要Python 3.10，安装依赖后使用，支持CUDA、NPU和CPU设备。

查看 README 依据

### Installation ```bash git clone https://github.com/Tencent/WeVisOne-VL.git && cd WeVisOne-VL conda create -n wevisone-vl python=3.10 -y && conda activate wevisone-vl pip install -r requirements.txt && pip install -v -e . ``` ### Inference ```python from PIL import Image from wevisone_vl import WeVisOneVL from wevisone_vl.visualization import save_result model = WeVisOneVL("tencent/WeVisOne-VL-4B", backend="transformers") # or backend="vllm" # model = WeVisOneVL("tencent/WeVisOne-VL-2B", backend="transformers") # Use 2B instead # Object-level: open-vocabulary detection

> `device` (`"auto"`, `"cuda:0"`, `"npu:0"`, `"cpu"`), `dtype`, `min_pixels` / `max_pixels`, and the usual decoding arguments (`max_new_tokens`, `temperature`, ...) are also available. The [Cookbooks](#-cookbooks) walk through each task in detail. ## 📓 Cookbooks | Tutorial | Covers | | --------------------------------------------------------------------------- | ---------------------------------------- |

已知限制

目前未提供微调代码，但推理包、Cookbooks和评估流程已发布。

查看 README 依据

- **[2026-10] 🔥🔥 We also open-sourced [Mask2Alpha](https://github.com/Tencent/WeVisOne-VL/tree/mask2alpha), the matting annotator used in the WeVisOne-VL Data Engine to turn binary masks into high-quality alpha mattes!** Check out the [`mask2alpha`](https://github.com/Tencent/WeVisOne-VL/tree/mask2alpha) branch. - **[2026-10]** 🎉 We release the technical report, model weights (2B & 4B), inference package, Cookbooks, and evaluation pipeline! ## 📝 TODO - [x] Inference package and Cookbooks - [x] Gradio web demo - [x] Benchmark evaluation pipeline

- [x] Model weights (2B & 4B) on Hugging Face - [x] Release our matting annotator - [ ] Fine-tuning code ## 🏆 Performance at a Glance Headline numbers for **WeVisOne-VL-4B** against the strongest open baseline in each benchmark (Qwen3-VL / Qwen3.5 up to 9B, Rex-Omni, LocateAnything, and task-specific segmentation / matting models). | Task | Benchmark | Metric | Best Baseline | **WeVisOne-VL-4B** | | ------------------ | -------------------- | ----------- | ------------- | ------------------ |