--- title: "VLX-Seek-1.5-10B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119" updated: "2026-11-13" --- # VLX-Seek-1.5-10B > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds and points at objects in images by choosing among candidate regions, and answers None when the target is absent. For drone, robot and surveillance camera vision work. VLX-Seek-1.5-10B is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by omlab ## About ### What it is VLX-Seek-1.5-10B is a 10B image and text model from omlab for finding and pointing at objects in a picture. It is built for drones, robots, robotic dogs, surveillance cameras and inspection systems. Instead of writing box coordinates as numbers, it picks from candidate regions of the image, and it can answer that a target is not there. ### Who it suits It suits teams that need a model to tell what is in a scene and which exact instance you mean. The card describes research and development use in fine-grained perception. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Robotics | Robot and robotic dog developers | Add region level perception to a robot's vision module | Locating the right cup among several in the robot's view | | Drones | Drone vision teams | Find small objects in dense scenes seen from above | Counting vehicles in a drone frame | | Security | Surveillance system builders | Ground a described object in camera frames | Finding the person a description refers to | | Inspection | Inspection system teams | Check whether a target is present or absent | Getting None back when the target is missing | You can skip it if you only need image captions, since it is aimed at answers tied to regions of the image. ### Where to use it - Python with the transformers library, on Windows, macOS or Linux - Drones, robots and robotic dogs - Surveillance cameras and inspection systems - Edge side devices or a robot base station | Use case | Who | What they get back | | --- | --- | --- | | Open vocabulary detection | Vision researchers | Located regions for the objects you name | | Referring expressions | Robotics developers | The one region that matches a description | | Object counting | Drone analysts | Counts backed by region level evidence | | Absent target checks | Inspection teams | An explicit None when nothing matches | ### How to set it up - Python and pip - Disk space: roughly 20 GB for the BF16 files, or roughly 5 GB for a 4-bit copy (estimated) - The project's GitHub repository from omlab, which holds the inference code 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open the om-ai-lab VLX-Seek repository on GitHub and follow its steps for installation and model loading. ```sh pip install -U huggingface_hub hf download omlab/VLX-Seek-1.5-10B ``` ### How to use it The model runs through a custom pipeline with four stages, and the card sends you to the repository for the code and runnable examples. That is where the install steps, inference scripts, prompt templates and visualization examples are. | Tool | What it does | | --- | --- | | Region proposals | Generates candidate regions in the image | | Region tokens | Turns each region into something the language model can refer to | | Prompt formatting | Builds the prompt that carries your question and the regions | | Post-processing | Converts the model's output into the final answer | ### Example Show it a camera frame and ask for the person in the red coat. It refers to the matching region, or answers None when nobody in the frame fits. ### For newcomers and experts You give it a picture and ask where something is. It answers by pointing at one of the regions it found, or it says None. For a first try, download the files with the two commands above and read the example scripts in the repository. Most vision language models write bounding box numbers out as text. This one turns candidate regions into addressable tokens and answers by selecting, comparing and referring to them, which fits what language models do well. Version 1.5 adds a stronger auxiliary vision tower, faster OPN proposal generation, more Linear Attention layers for speed and memory, and hard negative training for the None output. Omlab plans three sizes for the 1.5 family, 0.6B, 3B and 10B, and this page covers the largest. An image comes in with your question → Candidate regions are proposed → Regions become addressable tokens → The model selects and refers to regions → You get region answers or None ### Good to know The model works in Chinese and English. The card shows its results as pictured tables and a blog post, so no scores are quoted here. - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads model files from Hugging Face and works on the images you give it - Not for: work outside fine-grained perception, since the card lists only research and development uses in that area ## README ````markdown # VLX-Seek-1.5-10B Finds and points at objects in images by choosing among candidate regions, and answers None when the target is absent. For drone, robot and surveillance camera vision work. Made by omlab on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: VLX-Seek-1.5-10B is a 10B image and text model from omlab for finding and pointing at objects in a picture. It is built for drones, robots, robotic dogs, surveillance cameras and inspection systems. Instead of writing box coordinates as numbers, it picks from candidate regions of the image, and it can answer that a target is not there. Who it suits: It suits teams that need a model to tell what is in a scene and which exact instance you mean. The card describes research and development use in fine-grained perception. ```table Industry | Who uses it | How they use it | Example Robotics | Robot and robotic dog developers | Add region level perception to a robot's vision module | Locating the right cup among several in the robot's view Drones | Drone vision teams | Find small objects in dense scenes seen from above | Counting vehicles in a drone frame Security | Surveillance system builders | Ground a described object in camera frames | Finding the person a description refers to Inspection | Inspection system teams | Check whether a target is present or absent | Getting None back when the target is missing ``` You can skip it if you only need image captions, since it is aimed at answers tied to regions of the image. Where to use it: - Python with the transformers library, on Windows, macOS or Linux - Drones, robots and robotic dogs - Surveillance cameras and inspection systems - Edge side devices or a robot base station ```table Use case | Who | What they get back Open vocabulary detection | Vision researchers | Located regions for the objects you name Referring expressions | Robotics developers | The one region that matches a description Object counting | Drone analysts | Counts backed by region level evidence Absent target checks | Inspection teams | An explicit None when nothing matches ``` How to set it up: - Python and pip - Disk space: roughly 20 GB for the BF16 files, or roughly 5 GB for a 4-bit copy (estimated) - The project's GitHub repository from omlab, which holds the inference code 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open the om-ai-lab VLX-Seek repository on GitHub and follow its steps for installation and model loading. ```sh pip install -U huggingface_hub hf download omlab/VLX-Seek-1.5-10B ``` How to use it: The model runs through a custom pipeline with four stages, and the card sends you to the repository for the code and runnable examples. That is where the install steps, inference scripts, prompt templates and visualization examples are. ```table Tool | What it does Region proposals | Generates candidate regions in the image Region tokens | Turns each region into something the language model can refer to Prompt formatting | Builds the prompt that carries your question and the regions Post-processing | Converts the model's output into the final answer ``` Example: Show it a camera frame and ask for the person in the red coat. It refers to the matching region, or answers None when nobody in the frame fits. For newcomers and experts: You give it a picture and ask where something is. It answers by pointing at one of the regions it found, or it says None. For a first try, download the files with the two commands above and read the example scripts in the repository. Most vision language models write bounding box numbers out as text. This one turns candidate regions into addressable tokens and answers by selecting, comparing and referring to them, which fits what language models do well. Version 1.5 adds a stronger auxiliary vision tower, faster OPN proposal generation, more Linear Attention layers for speed and memory, and hard negative training for the None output. Omlab plans three sizes for the 1.5 family, 0.6B, 3B and 10B, and this page covers the largest. ```flow An image comes in with your question Candidate regions are proposed Regions become addressable tokens The model selects and refers to regions You get region answers or None ``` Good to know: The model works in Chinese and English. The card shows its results as pictured tables and a blog post, so no scores are quoted here. - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads model files from Hugging Face and works on the images you give it - Not for: work outside fine-grained perception, since the card lists only research and development uses in that area ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 10B - Weights: about 20 GB in BF16; about 5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 137,772 - Likes: 39 - License: apache-2.0 - Languages: zh, en - Quantized versions: 1 - Tags: transformers, safetensors, vlx_seek_1_5, text-generation, object-detection, grounding, multimodal, zero-shot-object-detection, image-text-to-text, conversational ## Links - Model: https://huggingface.co/omlab/VLX-Seek-1.5-10B - Files: https://huggingface.co/omlab/VLX-Seek-1.5-10B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download omlab/VLX-Seek-1.5-10B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [omlab/VLX-Seek-1.5-10B on Hugging Face](https://huggingface.co/omlab/VLX-Seek-1.5-10B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | VLX-Seek-1.5-10B (this page) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [WeVisOne-VL-4B](https://mindket.com/p/6d8cd6a2-35fb-4d9b-a126-1f4e12e81746) | 18,908 | 122 | 4.8B | apache-2.0 | 2026-10-27 | | [WeVisOne-VL-2B by tencent](https://mindket.com/p/9aa42bb9-4e2b-4689-bc45-2eaa0d7798c4) | 15,085 | 85 | 2.4B | apache-2.0 | 2026-10-28 | | [Sage-A1.5](https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd) | 22,224 | 404 | 36B | apache-2.0 | 2026-10-22 | | [Axion-1.0-35B](https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1) | 1,535 | 76 | 35.1B | apache-2.0 | 2026-11-04 | | [Tev-320B](https://mindket.com/p/63d40f1b-6af8-4909-9c96-9f1862166e30) | 495 | 17 | 321B | cc-by-nc-4.0 | 2026-10-14 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-07-23 - First published: 2026-07-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open omlab/VLX-Seek-1.5-10B on Hugging Face](https://huggingface.co/omlab/VLX-Seek-1.5-10B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) - [Its page on Mindket](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119)