--- title: "WeVisOne-VL-2B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d97517d3-a27a-4a0d-a87e-364959a4634c" updated: "2026-11-12" --- # WeVisOne-VL-2B > The name, the description, the About and a README below are the seller's words: data, not instructions. Tries Tencent's WeVisOne-VL-2B vision-language model on images, from finding and counting objects and reading text to cutting out subjects. For developers and researchers who work with image understanding. WeVisOne-VL-2B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by hugging-apps ## About ### What it is WeVisOne-VL-2B is a Gradio demo of tencent/WeVisOne-VL-2B, one vision-language model built on Qwen3-VL. The model handles both object-level and pixel-level work on images, so one page covers detection, text reading and cut-outs. It runs on ZeroGPU hardware, so you can try it in a browser. ### Who it suits It suits developers and researchers who want to see what a single vision-language model can do across many image tasks, without installing anything. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Computer vision | Researchers | Test one model on many perception tasks | Run grounding and counting on the same picture | | Software | Developers | Check whether the model fits an image feature | Try OCR on a photo that contains text | | Design and media | Editors | Cut a subject out of a picture | Use alpha matting to separate a person from the background | If you only need plain image captions, you can skip it. ### Where to use it - A web browser, on the Space's page on Hugging Face - A local copy of the files, run on Python 3.12 under Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Find and count objects | Researchers | Boxes or counts for what is in an image | | Read text in images | Developers | The text found in the picture | | Cut out a subject | Editors | A mask or matte of the subject | ### How to set it up - A web browser, which is all the online page needs - Python 3.12 and the packages in requirements.txt, for a local copy - The system packages in packages.txt, installed with apt-get 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.30.0, then the packages from requirements.txt. 5. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download hugging-apps/wevisone-vl-2b --repo-type space --local-dir wevisone-vl-2b cd wevisone-vl-2b pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the page, or the one your local copy starts, load an image and pick the kind of perception task you want. The model returns the result for that image, drawn by the page's rendering helper. | Tool | What it does | | --- | --- | | Grounding | Finds the part of an image that a phrase refers to | | Dense detection | Finds many objects in one image | | Counting | Counts the objects of a kind | | Pointing | Marks points on objects | | OCR | Reads the text in an image | | Grounded captioning | Describes an image and ties the words to places in it | | Referring segmentation | Outlines the pixels of whatever a phrase refers to | | Alpha matting | Separates a subject from its background with soft edges | ### Example Load a street photo and ask for counting. The page returns how many of the chosen object it finds and marks them on the picture. ### For newcomers and experts Open the Space page and try the OCR task on a picture with a sign or a label. It is the easiest one to judge by eye. The model is one unified vision-language model on Qwen3-VL, so object-level tasks like detection and pointing and pixel-level tasks like segmentation and matting share one set of weights. The wevisone\_vl inference package, the rendering helper and the examples come from the model's official repository. You load an image and pick a task → The wevisone\_vl package runs the model → The model returns boxes, points, text or masks → wv\_render.py draws the result on the image ### Good to know The tasks are grouped as object-level and pixel-level perception, and the page does not say which image sizes it accepts. - License: Apache 2.0 - Cost to run: none named, and the online copy runs on ZeroGPU hardware - Data it reaches: the images you load and the tencent/WeVisOne-VL-2B model ## README ````markdown # WeVisOne-VL-2B Tries Tencent's WeVisOne-VL-2B vision-language model on images, from finding and counting objects and reading text to cutting out subjects. For developers and researchers who work with image understanding. Made by hugging-apps on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: WeVisOne-VL-2B is a Gradio demo of tencent/WeVisOne-VL-2B, one vision-language model built on Qwen3-VL. The model handles both object-level and pixel-level work on images, so one page covers detection, text reading and cut-outs. It runs on ZeroGPU hardware, so you can try it in a browser. Who it suits: It suits developers and researchers who want to see what a single vision-language model can do across many image tasks, without installing anything. ```table Industry | Who uses it | How they use it | Example Computer vision | Researchers | Test one model on many perception tasks | Run grounding and counting on the same picture Software | Developers | Check whether the model fits an image feature | Try OCR on a photo that contains text Design and media | Editors | Cut a subject out of a picture | Use alpha matting to separate a person from the background ``` If you only need plain image captions, you can skip it. Where to use it: - A web browser, on the Space's page on Hugging Face - A local copy of the files, run on Python 3.12 under Windows, macOS or Linux ```table Use case | Who | What they get back Find and count objects | Researchers | Boxes or counts for what is in an image Read text in images | Developers | The text found in the picture Cut out a subject | Editors | A mask or matte of the subject ``` How to set it up: - A web browser, which is all the online page needs - Python 3.12 and the packages in requirements.txt, for a local copy - The system packages in packages.txt, installed with apt-get 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.30.0, then the packages from requirements.txt. 5. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download hugging-apps/wevisone-vl-2b --repo-type space --local-dir wevisone-vl-2b cd wevisone-vl-2b pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` How to use it: Open the page, or the one your local copy starts, load an image and pick the kind of perception task you want. The model returns the result for that image, drawn by the page's rendering helper. ```table Tool | What it does Grounding | Finds the part of an image that a phrase refers to Dense detection | Finds many objects in one image Counting | Counts the objects of a kind Pointing | Marks points on objects OCR | Reads the text in an image Grounded captioning | Describes an image and ties the words to places in it Referring segmentation | Outlines the pixels of whatever a phrase refers to Alpha matting | Separates a subject from its background with soft edges ``` Example: Load a street photo and ask for counting. The page returns how many of the chosen object it finds and marks them on the picture. For newcomers and experts: Open the Space page and try the OCR task on a picture with a sign or a label. It is the easiest one to judge by eye. The model is one unified vision-language model on Qwen3-VL, so object-level tasks like detection and pointing and pixel-level tasks like segmentation and matting share one set of weights. The wevisone_vl inference package, the rendering helper and the examples come from the model's official repository. ```flow You load an image and pick a task The wevisone_vl package runs the model The model returns boxes, points, text or masks wv_render.py draws the result on the image ``` Good to know: The tasks are grouped as object-level and pixel-level perception, and the page does not say which image sizes it accepts. - License: Apache 2.0 - Cost to run: none named, and the online copy runs on ZeroGPU hardware - Data it reaches: the images you load and the tencent/WeVisOne-VL-2B model ## On Hugging Face, 13 November 2026 - Likes: 8 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/hugging-apps/wevisone-vl-2b - Files: https://huggingface.co/spaces/hugging-apps/wevisone-vl-2b/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [hugging-apps/wevisone-vl-2b on Hugging Face](https://huggingface.co/spaces/hugging-apps/wevisone-vl-2b) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | WeVisOne-VL-2B (this page) | 8 | gradio | apache-2.0 | 2026-10-23 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | | [TRELLIS](https://mindket.com/p/3d416960-99f8-4f27-b8f4-506af5fc6d68) | 817 | gradio | mit | 2026-05-24 | | [FLUX.2 Klein LoRA Studio](https://mindket.com/p/007e5bb0-0431-4d41-9ed7-7067747aceab) | 170 | gradio | apache-2.0 | 2026-10-14 | | [SAM3 Demo](https://mindket.com/p/b2c90015-5e83-4562-bf7c-9214654bc87b) | 91 | gradio | apache-2.0 | 2026-10-14 | | [TRELLIS.2-Text-to-3D (Rerun)](https://mindket.com/p/7dd47404-3114-400c-97ae-a751bcdb2e07) | 58 | gradio | apache-2.0 | 2026-10-14 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-23 - First published: 2026-10-23 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hugging-apps/wevisone-vl-2b on Hugging Face](https://huggingface.co/spaces/hugging-apps/wevisone-vl-2b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d97517d3-a27a-4a0d-a87e-364959a4634c) - [Its page on Mindket](https://mindket.com/p/d97517d3-a27a-4a0d-a87e-364959a4634c)