--- title: "Image_Retrieval" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d780873f-bbb3-41fc-9929-781ee6b851e2" updated: "2026-11-13" --- # Image\_Retrieval > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds pictures similar to a query picture in a small holiday photo collection, with three saved methods named naive, bovw and cnn. For students and developers learning image retrieval. Image\_Retrieval 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 leopoldmaillard ## About ### What it is This Gradio app from leopoldmaillard is an image retrieval demo. The repository ships a folder of holiday pictures and three saved files named naive, bovw and cnn, which suggest three ways of matching images. You use it in a browser, or you run a copy on your own computer. ### Who it suits It suits students and developers who want to see how image search can work and compare simple matching with learned features. Teachers can show it as a live demo. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Computer vision students | Compare image matching methods on the same collection | Open the Space and try a query picture | | Research | Machine learning researchers | Look at a small retrieval setup with saved indexes | Read app.py next to the saved files | | Software | Python developers | Run a copy locally and adapt it to their own pictures | Download the Space and start app.py | Skip it if you need to search a large private photo library, because the repository holds a small sample collection. ### Where to use it - A web browser: go to the Space on Hugging Face and try it with no install - Windows, macOS or Linux: run a copy with Python | Use case | Who | What they get back | | --- | --- | --- | | Find similar pictures | Students | Pictures from the collection that match the query | | Compare methods | Researchers | A view of how the three saved methods differ | | Study the code | Developers | An app.py that loads saved files and a picture folder | ### How to set it up You need a few things in place first. - Python with pip, on Windows, macOS or Linux - An internet connection to fetch the files from Hugging Face - Disk space for a large repository, since it holds 1513 files 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio and the packages listed in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download leopoldmaillard/ImageRetrieval --repo-type space --local-dir ImageRetrieval cd ImageRetrieval pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser. ```sh python app.py ``` ### Example You run python app.py inside the project folder and open the page it serves. You give it a query picture, and the page shows pictures from the collection that it judges close to yours. ### For newcomers and experts If this is new to you, start online. Go to the Space and try one query picture before you install anything. A local copy is heavier, so try the hosted page first. If you know the field, note that the saved files named naive, bovw and cnn point to a plain baseline, a bag of visual words and a convolutional network. The source does not confirm this or give scores, so read app.py to check. Open the Space or run app.py → The Gradio page loads → You give it a query picture → The app matches it to the collection → Similar pictures appear on the page ### Good to know The repository holds saved pkl files that the app loads, so run a copy only from a source you trust. - License: mit - Cost to run: none named, no API key, account or paid service - Data it reaches: the pictures and saved files in the repository, plus your query picture ## README ````markdown # Image_Retrieval Finds pictures similar to a query picture in a small holiday photo collection, with three saved methods named naive, bovw and cnn. For students and developers learning image retrieval. Made by leopoldmaillard 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: This Gradio app from leopoldmaillard is an image retrieval demo. The repository ships a folder of holiday pictures and three saved files named naive, bovw and cnn, which suggest three ways of matching images. You use it in a browser, or you run a copy on your own computer. Who it suits: It suits students and developers who want to see how image search can work and compare simple matching with learned features. Teachers can show it as a live demo. ```table Industry | Who uses it | How they use it | Example Education | Computer vision students | Compare image matching methods on the same collection | Open the Space and try a query picture Research | Machine learning researchers | Look at a small retrieval setup with saved indexes | Read app.py next to the saved files Software | Python developers | Run a copy locally and adapt it to their own pictures | Download the Space and start app.py ``` Skip it if you need to search a large private photo library, because the repository holds a small sample collection. Where to use it: - A web browser: go to the Space on Hugging Face and try it with no install - Windows, macOS or Linux: run a copy with Python ```table Use case | Who | What they get back Find similar pictures | Students | Pictures from the collection that match the query Compare methods | Researchers | A view of how the three saved methods differ Study the code | Developers | An app.py that loads saved files and a picture folder ``` How to set it up: You need a few things in place first. - Python with pip, on Windows, macOS or Linux - An internet connection to fetch the files from Hugging Face - Disk space for a large repository, since it holds 1513 files 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio and the packages listed in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download leopoldmaillard/ImageRetrieval --repo-type space --local-dir ImageRetrieval cd ImageRetrieval pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, use the Gradio interface the Space page shows. On your own computer, run the main file from the project folder and open the page it serves in your browser. ```sh python app.py ``` Example: You run python app.py inside the project folder and open the page it serves. You give it a query picture, and the page shows pictures from the collection that it judges close to yours. For newcomers and experts: If this is new to you, start online. Go to the Space and try one query picture before you install anything. A local copy is heavier, so try the hosted page first. If you know the field, note that the saved files named naive, bovw and cnn point to a plain baseline, a bag of visual words and a convolutional network. The source does not confirm this or give scores, so read app.py to check. ```flow Open the Space or run app.py The Gradio page loads You give it a query picture The app matches it to the collection Similar pictures appear on the page ``` Good to know: The repository holds saved pkl files that the app loads, so run a copy only from a source you trust. - License: mit - Cost to run: none named, no API key, account or paid service - Data it reaches: the pictures and saved files in the repository, plus your query picture ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/leopoldmaillard/ImageRetrieval - Files: https://huggingface.co/spaces/leopoldmaillard/ImageRetrieval/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, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [leopoldmaillard/ImageRetrieval on Hugging Face](https://huggingface.co/spaces/leopoldmaillard/ImageRetrieval) ## 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 | | --- | --- | --- | --- | --- | | Image\_Retrieval (this page) | 1 | gradio | mit | 2022-03-13 | | [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: 2022-03-13 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open leopoldmaillard/ImageRetrieval on Hugging Face](https://huggingface.co/spaces/leopoldmaillard/ImageRetrieval) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d780873f-bbb3-41fc-9929-781ee6b851e2) - [Its page on Mindket](https://mindket.com/p/d780873f-bbb3-41fc-9929-781ee6b851e2)