--- title: "MONET Retrieval" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4619e85e-7f2b-4660-aef7-e3a3ba4d9e96" updated: "2026-11-12" --- # MONET Retrieval > The name, the description, the About and a README below are the seller's words: data, not instructions. A Gradio app from jasperai that finds the nearest matches in the MONET dataset from an uploaded image or a caption keyword, using DINOv2, CLIP or SSCD embeddings. MONET Retrieval 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 jasperai ## About ### What it is MONET Retrieval is a self-contained Gradio app from jasperai that searches the MONET dataset by nearest neighbour. You can upload an image and get its closest matches, or type keywords and search captions. It queries precomputed FAISS indices, and it runs on zero-a10g hardware at Hugging Face. ### Who it suits Researchers who work with the MONET dataset and want to look up similar items, and developers who want a worked example of an embedding search app in Gradio. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Dataset researchers | Upload an image and look at its nearest neighbours | Finding items in the dataset that resemble a query picture | | Machine learning | Embedding engineers | Compare how DINOv2, CLIP and SSCD rank the same query | Seeing which embedder returns the closest matches | | Software development | Gradio developers | Read app.py and embedders.py | Studying how three embedders are loaded and queried | Skip it if you want a general image search over the web, since it only searches the MONET dataset. ### Where to use it MONET Retrieval runs in these places. - A web browser: open the Space's page on Hugging Face - Python 3.12.12 on Windows, macOS or Linux: run a copy of the Space | Use case | Who | What they get back | | --- | --- | --- | | Search by image | Researchers | The k nearest neighbours of an uploaded image, as thumbnails | | Search by caption | Researchers | Keyword matches found through CLIP text embeddings | | Read a record | Anyone | Full record metadata, shown when you click a thumbnail | ### How to set it up Online use needs only a browser. A local copy needs more, because the app expects its index files in a set layout. - A web browser for the online Space - Python 3.12.12 on Windows, macOS or Linux for a local copy - The Python packages listed in requirements.txt - The index files, shipped in a storage bucket named jasperai/monet-retrieval-storage 1. Install the Hugging Face hub library with pip. 2. Download the Space's files into a folder called monet-retrieval. 3. Move into that folder. 4. Install Gradio at the version the Space uses. 5. Install the packages listed in requirements.txt. 6. Make the index files available, then start the app with Python. ```sh pip install -U huggingface_hub hf download jasperai/monet-retrieval --repo-type space --local-dir monet-retrieval cd monet-retrieval pip install gradio==6.13.0 pip install -r requirements.txt python app.py ``` The app reads optional environment variables. These are the main ones. | Name | Default | What it does | | --- | --- | --- | | MONET\_BUCKET\_MOUNT | /data | Where the storage bucket is mounted | | MONET\_DATA\_ROOT | next to app.py | The folder the bucket files are copied into | | MONET\_MODELS | dinov2,clip,sscd | Which embedders to load | | MONET\_SKIP\_SYNC | off | Set to 1 to read index files straight from the bucket instead of copying them | | MONET\_HF\_REPO | jasperai/monet | The dataset that thumbnails are fetched from | | HF\_TOKEN | none | Hub access for private datasets | | MONET\_DOWNLOAD\_WORKERS | 16 | How many thumbnail fetches run in parallel | | MONET\_SYNC\_WORKERS | 4 | How many copies run in parallel during the bucket sync | | MONET\_PARQUET\_CACHE\_BUDGET\_GB | 8 | Size cap for the fallback cache, where 0 turns caching off | | MONET\_PARQUET\_CACHE\_FREE\_FLOOR\_GB | 2 | Free disk level below which the cache is cleared | ### How to use it Online, open the Space's page and pick a search mode. On your own computer, start the app from the folder with the command below. ```sh python app.py ``` | Tool | What it does | | --- | --- | | Search by image | Embeds an uploaded image with DINOv2, CLIP or SSCD and returns its nearest neighbours | | Search by caption | Runs a keyword search through CLIP text embeddings | | Thumbnail click | Shows the full record metadata for that item | ### Example You upload a photo and ask for its nearest neighbours with one of the three embedders. The app returns matching thumbnails, and clicking one shows that item's full record metadata. ### For newcomers and experts If you are new, open the Space's page and upload a picture. An embedding turns an image into numbers, and the app finds the entries whose numbers are closest. If you know the field, the app preloads its three embedders at launch, so even the first query skips model loading. Thumbnails are range-read from the upstream parquet files on the Hub, so no full downloads are needed. A shared alignment table maps rows across the three models, which keeps the app from loading the large hash files. Skipping the bucket sync is recommended on Hugging Face Spaces, where the disk is too small for more than 24 GB of index files. Open the Space or run app.py → Upload an image or type keywords → The embedder turns the query into numbers → FAISS finds the nearest neighbours → Thumbnails appear with their metadata ### Good to know It searches only the MONET dataset, and it needs its index files to work. - Version: Gradio 6.13.0 and Python 3.12.12 - Embedders: DINOv2, CLIP and SSCD, where CLIP alone is named for text search - Size: 8 files in the repository, plus the separate index bucket - License: cc-by-4 - Cost to run: none named, but the index files run past 24 GB - Data it reaches: the MONET dataset files on the Hugging Face Hub, the index bucket and the SSCD checkpoint, which is fetched on first use - Not for: no forbidden uses are named ## README ````markdown # MONET Retrieval A Gradio app from jasperai that finds the nearest matches in the MONET dataset from an uploaded image or a caption keyword, using DINOv2, CLIP or SSCD embeddings. Made by jasperai 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: MONET Retrieval is a self-contained Gradio app from jasperai that searches the MONET dataset by nearest neighbour. You can upload an image and get its closest matches, or type keywords and search captions. It queries precomputed FAISS indices, and it runs on zero-a10g hardware at Hugging Face. Who it suits: Researchers who work with the MONET dataset and want to look up similar items, and developers who want a worked example of an embedding search app in Gradio. ```table Industry | Who uses it | How they use it | Example Research | Dataset researchers | Upload an image and look at its nearest neighbours | Finding items in the dataset that resemble a query picture Machine learning | Embedding engineers | Compare how DINOv2, CLIP and SSCD rank the same query | Seeing which embedder returns the closest matches Software development | Gradio developers | Read app.py and embedders.py | Studying how three embedders are loaded and queried ``` Skip it if you want a general image search over the web, since it only searches the MONET dataset. Where to use it: MONET Retrieval runs in these places. - A web browser: open the Space's page on Hugging Face - Python 3.12.12 on Windows, macOS or Linux: run a copy of the Space ```table Use case | Who | What they get back Search by image | Researchers | The k nearest neighbours of an uploaded image, as thumbnails Search by caption | Researchers | Keyword matches found through CLIP text embeddings Read a record | Anyone | Full record metadata, shown when you click a thumbnail ``` How to set it up: Online use needs only a browser. A local copy needs more, because the app expects its index files in a set layout. - A web browser for the online Space - Python 3.12.12 on Windows, macOS or Linux for a local copy - The Python packages listed in requirements.txt - The index files, shipped in a storage bucket named jasperai/monet-retrieval-storage 1. Install the Hugging Face hub library with pip. 2. Download the Space's files into a folder called monet-retrieval. 3. Move into that folder. 4. Install Gradio at the version the Space uses. 5. Install the packages listed in requirements.txt. 6. Make the index files available, then start the app with Python. ```sh pip install -U huggingface_hub hf download jasperai/monet-retrieval --repo-type space --local-dir monet-retrieval cd monet-retrieval pip install gradio==6.13.0 pip install -r requirements.txt python app.py ``` The app reads optional environment variables. These are the main ones. ```table Name | Default | What it does MONET_BUCKET_MOUNT | /data | Where the storage bucket is mounted MONET_DATA_ROOT | next to app.py | The folder the bucket files are copied into MONET_MODELS | dinov2,clip,sscd | Which embedders to load MONET_SKIP_SYNC | off | Set to 1 to read index files straight from the bucket instead of copying them MONET_HF_REPO | jasperai/monet | The dataset that thumbnails are fetched from HF_TOKEN | none | Hub access for private datasets MONET_DOWNLOAD_WORKERS | 16 | How many thumbnail fetches run in parallel MONET_SYNC_WORKERS | 4 | How many copies run in parallel during the bucket sync MONET_PARQUET_CACHE_BUDGET_GB | 8 | Size cap for the fallback cache, where 0 turns caching off MONET_PARQUET_CACHE_FREE_FLOOR_GB | 2 | Free disk level below which the cache is cleared ``` How to use it: Online, open the Space's page and pick a search mode. On your own computer, start the app from the folder with the command below. ```sh python app.py ``` ```table Tool | What it does Search by image | Embeds an uploaded image with DINOv2, CLIP or SSCD and returns its nearest neighbours Search by caption | Runs a keyword search through CLIP text embeddings Thumbnail click | Shows the full record metadata for that item ``` Example: You upload a photo and ask for its nearest neighbours with one of the three embedders. The app returns matching thumbnails, and clicking one shows that item's full record metadata. For newcomers and experts: If you are new, open the Space's page and upload a picture. An embedding turns an image into numbers, and the app finds the entries whose numbers are closest. If you know the field, the app preloads its three embedders at launch, so even the first query skips model loading. Thumbnails are range-read from the upstream parquet files on the Hub, so no full downloads are needed. A shared alignment table maps rows across the three models, which keeps the app from loading the large hash files. Skipping the bucket sync is recommended on Hugging Face Spaces, where the disk is too small for more than 24 GB of index files. ```flow Open the Space or run app.py Upload an image or type keywords The embedder turns the query into numbers FAISS finds the nearest neighbours Thumbnails appear with their metadata ``` Good to know: It searches only the MONET dataset, and it needs its index files to work. - Version: Gradio 6.13.0 and Python 3.12.12 - Embedders: DINOv2, CLIP and SSCD, where CLIP alone is named for text search - Size: 8 files in the repository, plus the separate index bucket - License: cc-by-4 - Cost to run: none named, but the index files run past 24 GB - Data it reaches: the MONET dataset files on the Hugging Face Hub, the index bucket and the SSCD checkpoint, which is fetched on first use - Not for: no forbidden uses are named ## On Hugging Face, 13 November 2026 - Likes: 20 - Built with: gradio - License: cc-by-4.0 ## Links - Space: https://huggingface.co/spaces/jasperai/monet-retrieval - Files: https://huggingface.co/spaces/jasperai/monet-retrieval/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, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [jasperai/monet-retrieval on Hugging Face](https://huggingface.co/spaces/jasperai/monet-retrieval) ## 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 | | --- | --- | --- | --- | --- | | MONET Retrieval (this page) | 20 | gradio | cc-by-4.0 | 2026-06-30 | | [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: 2026-06-30 - First published: 2026-04-29 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open jasperai/monet-retrieval on Hugging Face](https://huggingface.co/spaces/jasperai/monet-retrieval) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4619e85e-7f2b-4660-aef7-e3a3ba4d9e96) - [Its page on Mindket](https://mindket.com/p/4619e85e-7f2b-4660-aef7-e3a3ba4d9e96)