--- title: "Rabbit Pain Identifier" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6a94aff6-00a7-4f08-bd9d-3dc8b42a7119" updated: "2026-11-12" --- # Rabbit Pain Identifier > The name, the description, the About and a README below are the seller's words: data, not instructions. Screens a single rabbit photo for signs of pain by measuring 23 facial landmarks and labelling it Relaxed or Pain with a probability. A research prototype for rabbit owners, vets and students, not a diagnosis. Rabbit Pain Identifier 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 sheildo ## About ### What it is This Space looks at one photo of a rabbit and estimates whether the animal seems relaxed or in pain. It finds 23 landmarks on the face, corrects for head tilt, measures facial geometry and passes the numbers to a classifier. The result is a label with a probability score. ### Who it suits Rabbit owners, veterinary students and researchers who want to see how photo-based pain screening works. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Veterinary research | Animal welfare researchers | Study facial geometry as a pain signal | Run a photo and read the probability score | | Veterinary education | Students | Learn how a screening model is built | Open the sample photos and compare the labels | | Pet care | Rabbit owners | Get a first, informal look at a photo | Upload a clear front-facing photo of their rabbit | | Software | ML developers | Study a small landmark and KNN pipeline | Read app.py next to the saved model files | If you need a diagnosis, ask a vet. The page says it is a research prototype and not a diagnosis. ### Where to use it It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python 3.12 | Use case | Who | What they get back | | --- | --- | --- | | Screen one photo | Owners and vets | A Relaxed or Pain label with a probability | | Try the samples | Students | Results for the lop ear, pain and relaxed sample photos | | Study the pipeline | Developers | The model files and the app code in one repository | ### How to set it up Using it online needs nothing. A local copy needs a few things. - Python 3.12 and pip - The packages listed in requirements.txt - The system packages listed in packages.txt, installed with apt-get 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download sheildo/Rabbit-Pain-Identifier-Model --repo-type space --local-dir Rabbit-Pain-Identifier-Model cd Rabbit-Pain-Identifier-Model pip install gradio==5.49.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the page and add a single photo of a rabbit. The page returns a Relaxed or Pain label and the raw probability. For screening, where you want to catch every possible pain case, use a threshold of 0.4. For triage, where you want fewer false alarms, use 0.5. ### Example You upload a clear photo of a rabbit with upright ears. The page marks the facial landmarks and reports a label with a probability, which you compare against the 0.4 or 0.5 threshold that suits your goal. ### For newcomers and experts Start with one of the sample photos in the repository to see how a result looks. Remember that a photo is a rough guide and a clinician has the final say. The pipeline uses a YOLOv8n-pose model to find 23 landmarks. It turns the landmarks around the point between the eyes until the eyes sit level, and it scales every measurement by how far apart the eyes are. For floppy-eared rabbits, an ear drop check turns off the ear features. A KNN classifier then predicts the label. YOLOv8n-pose finds 23 facial landmarks → Landmarks are rotated to level the eyes → Features are scaled by the eye distance → A KNN classifier scores Relaxed or Pain ### Good to know The model was trained on one curated set of 620 images, and the author says outside validation is still pending. Accuracy of the classifier on its checks | Label | Value | | --- | --- | | Test set | 0.895 accuracy | | Validation set | 0.871 accuracy | | 5-fold cross-validation | 0.820 accuracy | | Majority baseline | 0.659 accuracy | - Each photo takes about 2 to 6 seconds on the free CPU tier - Results come from a research prototype and are not a veterinary diagnosis - License: MIT - Cost to run: none named - Data it reaches: the photos you upload - Not for: veterinary diagnosis, which belongs with a clinician and the Rabbit Grimace Scale ## README ````markdown # Rabbit Pain Identifier Screens a single rabbit photo for signs of pain by measuring 23 facial landmarks and labelling it Relaxed or Pain with a probability. A research prototype for rabbit owners, vets and students, not a diagnosis. Made by sheildo 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 Space looks at one photo of a rabbit and estimates whether the animal seems relaxed or in pain. It finds 23 landmarks on the face, corrects for head tilt, measures facial geometry and passes the numbers to a classifier. The result is a label with a probability score. Who it suits: Rabbit owners, veterinary students and researchers who want to see how photo-based pain screening works. ```table Industry | Who uses it | How they use it | Example Veterinary research | Animal welfare researchers | Study facial geometry as a pain signal | Run a photo and read the probability score Veterinary education | Students | Learn how a screening model is built | Open the sample photos and compare the labels Pet care | Rabbit owners | Get a first, informal look at a photo | Upload a clear front-facing photo of their rabbit Software | ML developers | Study a small landmark and KNN pipeline | Read app.py next to the saved model files ``` If you need a diagnosis, ask a vet. The page says it is a research prototype and not a diagnosis. Where to use it: It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python 3.12 ```table Use case | Who | What they get back Screen one photo | Owners and vets | A Relaxed or Pain label with a probability Try the samples | Students | Results for the lop ear, pain and relaxed sample photos Study the pipeline | Developers | The model files and the app code in one repository ``` How to set it up: Using it online needs nothing. A local copy needs a few things. - Python 3.12 and pip - The packages listed in requirements.txt - The system packages listed in packages.txt, installed with apt-get 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download sheildo/Rabbit-Pain-Identifier-Model --repo-type space --local-dir Rabbit-Pain-Identifier-Model cd Rabbit-Pain-Identifier-Model pip install gradio==5.49.0 pip install -r requirements.txt python app.py ``` How to use it: Open the page and add a single photo of a rabbit. The page returns a Relaxed or Pain label and the raw probability. For screening, where you want to catch every possible pain case, use a threshold of 0.4. For triage, where you want fewer false alarms, use 0.5. Example: You upload a clear photo of a rabbit with upright ears. The page marks the facial landmarks and reports a label with a probability, which you compare against the 0.4 or 0.5 threshold that suits your goal. For newcomers and experts: Start with one of the sample photos in the repository to see how a result looks. Remember that a photo is a rough guide and a clinician has the final say. The pipeline uses a YOLOv8n-pose model to find 23 landmarks. It turns the landmarks around the point between the eyes until the eyes sit level, and it scales every measurement by how far apart the eyes are. For floppy-eared rabbits, an ear drop check turns off the ear features. A KNN classifier then predicts the label. ```flow YOLOv8n-pose finds 23 facial landmarks Landmarks are rotated to level the eyes Features are scaled by the eye distance A KNN classifier scores Relaxed or Pain ``` Good to know: The model was trained on one curated set of 620 images, and the author says outside validation is still pending. ```chart Title: Accuracy of the classifier on its checks Test set: 0.895 accuracy Validation set: 0.871 accuracy 5-fold cross-validation: 0.820 accuracy Majority baseline: 0.659 accuracy ``` - Each photo takes about 2 to 6 seconds on the free CPU tier - Results come from a research prototype and are not a veterinary diagnosis - License: MIT - Cost to run: none named - Data it reaches: the photos you upload - Not for: veterinary diagnosis, which belongs with a clinician and the Rabbit Grimace Scale ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/sheildo/Rabbit-Pain-Identifier-Model - Files: https://huggingface.co/spaces/sheildo/Rabbit-Pain-Identifier-Model/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 [sheildo/Rabbit-Pain-Identifier-Model on Hugging Face](https://huggingface.co/spaces/sheildo/Rabbit-Pain-Identifier-Model) ## 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 | | --- | --- | --- | --- | --- | | Rabbit Pain Identifier (this page) | 1 | gradio | mit | 2026-11-09 | | [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: 2026-11-09 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sheildo/Rabbit-Pain-Identifier-Model on Hugging Face](https://huggingface.co/spaces/sheildo/Rabbit-Pain-Identifier-Model) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6a94aff6-00a7-4f08-bd9d-3dc8b42a7119) - [Its page on Mindket](https://mindket.com/p/6a94aff6-00a7-4f08-bd9d-3dc8b42a7119)