--- title: "Car Part Classification" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/53e95b89-07aa-47e1-aed0-684d8e48f5ff" updated: "2026-11-12" --- # Car Part Classification > The name, the description, the About and a README below are the seller's words: data, not instructions. A Streamlit app from Detomo for identifying car parts from pictures, with an ONNX model and saved embeddings in its repository. For people exploring image classification. Car Part Classification 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 Detomo ## About ### What it is Car Part Classification is a Streamlit app from Detomo. As its name says, it is meant to recognize car parts in pictures. The repository holds an ONNX model file, saved embeddings, a test set and a CSV of the full set. The README carries only the standard Space settings text. ### Who it suits It suits people who want to see a small image classification demo for a narrow subject, and developers who want its files as a starting point. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Automotive | Parts catalog staff | Try matching a picture to a part type | Upload a photo of a part and read the result | | Machine learning | Students | Study an image classification demo | Look at how the model and embeddings are stored | | Research | Computer vision researchers | Review a small domain dataset setup | Read the set files and the training list | | E-commerce | Marketplace builders | Test sorting part photos | Try a few product photos on the demo | You can skip it if you need a documented classifier with published accuracy. ### Where to use it - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Identify a part | Newcomers | A guess at the part type from a picture | | Study the files | Students | A model, embeddings and a test set to read | | Reuse the setup | Developers | A Streamlit app wrapped around an ONNX model | ### How to set it up - Nothing to install if you use the Space online - Python, if you run a copy on your own computer - The Python packages in requirements.txt - The system packages in packages.txt, which Hugging Face installs with apt-get 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface\_hub and download the Space into a folder. It holds more than a thousand files, so the download takes a while. 3. Move into that folder and install Streamlit, then the listed packages. 4. Start the app with Streamlit. ```sh pip install -U huggingface_hub hf download Detomo/Car_part_classification --repo-type space --local-dir Car_part_classification cd Car_part_classification pip install streamlit pip install -r requirements.txt streamlit run app.py ``` ### How to use it Open the Space and give it a picture of a car part, if the page asks for one. The README gives no steps, so use the page's own prompts. On your own copy, start the app with this command and open the local page Streamlit prints. ```sh streamlit run app.py ``` ### Example Open the Space online and give it a photo of a car part. You should get back a guess at which part it is, though the README does not describe the output. ### For newcomers and experts If you are new, open the Space online and try one clear photo of a single part. Plain pictures with the part in the middle are the easiest case. If you know the field, the repository holds an ONNX model, embeddings in two formats, a test set, a training list and helper scripts. That points to an embedding-based approach, but the README does not confirm how the app classifies. It also does not state accuracy or classes. You give the app a car part picture → The ONNX model processes the image → The app compares it with its stored data → The page shows its guess ### Good to know - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face. - Data it reaches: the pictures you give it, and the model and dataset files in its repository. ## README ````markdown # Car Part Classification A Streamlit app from Detomo for identifying car parts from pictures, with an ONNX model and saved embeddings in its repository. For people exploring image classification. Made by Detomo 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: Car Part Classification is a Streamlit app from Detomo. As its name says, it is meant to recognize car parts in pictures. The repository holds an ONNX model file, saved embeddings, a test set and a CSV of the full set. The README carries only the standard Space settings text. Who it suits: It suits people who want to see a small image classification demo for a narrow subject, and developers who want its files as a starting point. ```table Industry | Who uses it | How they use it | Example Automotive | Parts catalog staff | Try matching a picture to a part type | Upload a photo of a part and read the result Machine learning | Students | Study an image classification demo | Look at how the model and embeddings are stored Research | Computer vision researchers | Review a small domain dataset setup | Read the set files and the training list E-commerce | Marketplace builders | Test sorting part photos | Try a few product photos on the demo ``` You can skip it if you need a documented classifier with published accuracy. Where to use it: - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux ```table Use case | Who | What they get back Identify a part | Newcomers | A guess at the part type from a picture Study the files | Students | A model, embeddings and a test set to read Reuse the setup | Developers | A Streamlit app wrapped around an ONNX model ``` How to set it up: - Nothing to install if you use the Space online - Python, if you run a copy on your own computer - The Python packages in requirements.txt - The system packages in packages.txt, which Hugging Face installs with apt-get 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface_hub and download the Space into a folder. It holds more than a thousand files, so the download takes a while. 3. Move into that folder and install Streamlit, then the listed packages. 4. Start the app with Streamlit. ```sh pip install -U huggingface_hub hf download Detomo/Car_part_classification --repo-type space --local-dir Car_part_classification cd Car_part_classification pip install streamlit pip install -r requirements.txt streamlit run app.py ``` How to use it: Open the Space and give it a picture of a car part, if the page asks for one. The README gives no steps, so use the page's own prompts. On your own copy, start the app with this command and open the local page Streamlit prints. ```sh streamlit run app.py ``` Example: Open the Space online and give it a photo of a car part. You should get back a guess at which part it is, though the README does not describe the output. For newcomers and experts: If you are new, open the Space online and try one clear photo of a single part. Plain pictures with the part in the middle are the easiest case. If you know the field, the repository holds an ONNX model, embeddings in two formats, a test set, a training list and helper scripts. That points to an embedding-based approach, but the README does not confirm how the app classifies. It also does not state accuracy or classes. ```flow You give the app a car part picture The ONNX model processes the image The app compares it with its stored data The page shows its guess ``` Good to know: - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face. - Data it reaches: the pictures you give it, and the model and dataset files in its repository. ## On Hugging Face, 13 November 2026 - Likes: 4 - Built with: streamlit - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/Detomo/Car_part_classification - Files: https://huggingface.co/spaces/Detomo/Car_part_classification/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 [Detomo/Car_part_classification on Hugging Face](https://huggingface.co/spaces/Detomo/Car_part_classification) ## 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 | | --- | --- | --- | --- | --- | | Car Part Classification (this page) | 4 | streamlit | apache-2.0 | 2024-03-11 | | [AI Phone Leaderboard](https://mindket.com/p/c50d4420-a218-400f-917a-9fa39c2d8007) | 127 | streamlit | mit | 2026-02-04 | | [fev-bench](https://mindket.com/p/aed1efeb-102a-49fd-b48b-0e7131fb1d85) | 72 | docker | apache-2.0 | 2026-11-09 | | [Outsmart](https://mindket.com/p/bc535e3d-8da3-4481-bb74-8405891098d4) | 62 | streamlit | mit | 2026-10-21 | | [HF BERTopic](https://mindket.com/p/6ea227ee-d2c5-4685-886d-6070ad67ac1f) | 24 | streamlit | mit | 2026-08-02 | | [HF SHAP](https://mindket.com/p/aa7ec1ee-7a27-4fe5-9191-d22a0af5d958) | 8 | streamlit | mit | 2026-08-02 | 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: 2024-03-11 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Detomo/Car_part_classification on Hugging Face](https://huggingface.co/spaces/Detomo/Car_part_classification) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/53e95b89-07aa-47e1-aed0-684d8e48f5ff) - [Its page on Mindket](https://mindket.com/p/53e95b89-07aa-47e1-aed0-684d8e48f5ff)