--- title: "GeCo2 Object Counting" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/960cccbf-71a9-4b77-ac03-be50904ba573" updated: "2026-11-12" --- # GeCo2 Object Counting > The name, the description, the About and a README below are the seller's words: data, not instructions. Counts all instances of an object in an image after you mark a few examples, with no retraining or fixed list of classes. Suits researchers and anyone counting items in photos. GeCo2 Object Counting is a kit in the Research 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 jerpelhan ## About ### What it is GeCo2 counts objects in a picture. You mark a few examples of the thing you care about, and it finds and counts every matching instance in the image. It needs no fixed list of object classes and no retraining. This Space is a Gradio demo of the GeCo2 model, and it is based on an earlier counting demo called CountGD. ### Who it suits It suits researchers working on few-shot counting, and people who need to count unusual objects where labelled data is scarce. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Retail and stock | Inventory staff | Count items shown in a photo | Mark one box on a shelf and count the rest | | Agriculture | Growers and agronomists | Count plants or fruit in an image | Mark two fruits and count the others in the tree photo | | Lab work | Technicians | Count small objects when no trained counter exists | Mark a few samples and count all similar ones in a picture | | Research | Computer vision researchers | Try few-shot counting on new object types | Compare counts from different example choices | Skip it if you want a counter that needs no examples, since this one asks you to mark a few. ### Where to use it - A browser: open the Space's page on Hugging Face, with nothing to install - A copy on Windows, macOS or Linux, using Python 3.10.13 | Use case | Who | What they get back | | --- | --- | --- | | Counting objects in a photo | Anyone with a picture | All matching instances found and counted | | Exploring a new category | Researchers | A quick count without training a new model | | Reading the code | Developers | A repository with SAM 2 and Deformable-DETR folders | ### How to set it up The online copy needs nothing. For a copy on your own computer, you need the following. - Python 3.10.13 with pip on Windows, macOS or Linux - The Hugging Face hub client, which the first step installs - The packages listed in requirements.txt 1. Install the Hugging Face hub client. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install the Gradio version the Space pins. 5. Install the remaining packages from requirements.txt. 6. Start demo\_gradio.py. ```sh pip install -U huggingface_hub hf download jerpelhan/GECO2-demo --repo-type space --local-dir GECO2-demo cd GECO2-demo pip install gradio==5.50.0 pip install -r requirements.txt python demo_gradio.py ``` ### How to use it Upload an image, then mark a few examples of the object you want counted. GeCo2 spreads that idea across the picture and counts the matches. The source does not describe the exact controls, so follow the labels on the page. ```sh python demo_gradio.py ``` ### Example You upload a photo of a bowl of oranges and mark two or three of them as examples. The Space returns the photo with the matching oranges detected, so you can read off how many there are. The source gives no accuracy figures. ### For newcomers and experts If you are new, pick a picture with many similar objects in clear view and mark two or three clean examples. Add one more example if the first count looks wrong. If you know the field, GeCo2 detects and counts by learning the target from a handful of exemplars, with no fixed class list, then applying it over the whole image. The Space names four SAM 2 Hiera sizes, from tiny to large, and runs on zero-a10g hardware. The paper's claims on scale variation and varied domains are the author's own. You upload an image → You mark a few example objects → GeCo2 spreads the concept over the image → Matching objects are detected and counted ### Good to know The source is a demo page, so speed and accuracy limits are not described. - License: mit, as listed on Hugging Face - Cost to run: no API key or paid service is named, and the online copy runs on zero-a10g hardware at Hugging Face - Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, plus an assets dataset the Space names - Not for: the source lists no exclusions, so use only pictures you have the right to use ## README ````markdown # GeCo2 Object Counting Counts all instances of an object in an image after you mark a few examples, with no retraining or fixed list of classes. Suits researchers and anyone counting items in photos. Made by jerpelhan 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: GeCo2 counts objects in a picture. You mark a few examples of the thing you care about, and it finds and counts every matching instance in the image. It needs no fixed list of object classes and no retraining. This Space is a Gradio demo of the GeCo2 model, and it is based on an earlier counting demo called CountGD. Who it suits: It suits researchers working on few-shot counting, and people who need to count unusual objects where labelled data is scarce. ```table Industry | Who uses it | How they use it | Example Retail and stock | Inventory staff | Count items shown in a photo | Mark one box on a shelf and count the rest Agriculture | Growers and agronomists | Count plants or fruit in an image | Mark two fruits and count the others in the tree photo Lab work | Technicians | Count small objects when no trained counter exists | Mark a few samples and count all similar ones in a picture Research | Computer vision researchers | Try few-shot counting on new object types | Compare counts from different example choices ``` Skip it if you want a counter that needs no examples, since this one asks you to mark a few. Where to use it: - A browser: open the Space's page on Hugging Face, with nothing to install - A copy on Windows, macOS or Linux, using Python 3.10.13 ```table Use case | Who | What they get back Counting objects in a photo | Anyone with a picture | All matching instances found and counted Exploring a new category | Researchers | A quick count without training a new model Reading the code | Developers | A repository with SAM 2 and Deformable-DETR folders ``` How to set it up: The online copy needs nothing. For a copy on your own computer, you need the following. - Python 3.10.13 with pip on Windows, macOS or Linux - The Hugging Face hub client, which the first step installs - The packages listed in requirements.txt 1. Install the Hugging Face hub client. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install the Gradio version the Space pins. 5. Install the remaining packages from requirements.txt. 6. Start demo_gradio.py. ```sh pip install -U huggingface_hub hf download jerpelhan/GECO2-demo --repo-type space --local-dir GECO2-demo cd GECO2-demo pip install gradio==5.50.0 pip install -r requirements.txt python demo_gradio.py ``` How to use it: Upload an image, then mark a few examples of the object you want counted. GeCo2 spreads that idea across the picture and counts the matches. The source does not describe the exact controls, so follow the labels on the page. ```sh python demo_gradio.py ``` Example: You upload a photo of a bowl of oranges and mark two or three of them as examples. The Space returns the photo with the matching oranges detected, so you can read off how many there are. The source gives no accuracy figures. For newcomers and experts: If you are new, pick a picture with many similar objects in clear view and mark two or three clean examples. Add one more example if the first count looks wrong. If you know the field, GeCo2 detects and counts by learning the target from a handful of exemplars, with no fixed class list, then applying it over the whole image. The Space names four SAM 2 Hiera sizes, from tiny to large, and runs on zero-a10g hardware. The paper's claims on scale variation and varied domains are the author's own. ```flow You upload an image You mark a few example objects GeCo2 spreads the concept over the image Matching objects are detected and counted ``` Good to know: The source is a demo page, so speed and accuracy limits are not described. - License: mit, as listed on Hugging Face - Cost to run: no API key or paid service is named, and the online copy runs on zero-a10g hardware at Hugging Face - Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, plus an assets dataset the Space names - Not for: the source lists no exclusions, so use only pictures you have the right to use ## On Hugging Face, 13 November 2026 - Likes: 11 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/jerpelhan/GECO2-demo - Files: https://huggingface.co/spaces/jerpelhan/GECO2-demo/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 [jerpelhan/GECO2-demo on Hugging Face](https://huggingface.co/spaces/jerpelhan/GECO2-demo) ## 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 | | --- | --- | --- | --- | --- | | GeCo2 Object Counting (this page) | 11 | gradio | mit | 2026-05-13 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | 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: 2026-05-13 - First published: 2025-12-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open jerpelhan/GECO2-demo on Hugging Face](https://huggingface.co/spaces/jerpelhan/GECO2-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/960cccbf-71a9-4b77-ac03-be50904ba573) - [Its page on Mindket](https://mindket.com/p/960cccbf-71a9-4b77-ac03-be50904ba573)