Human Part Segmentation

Human Part Segmentation

Labels the parts of a person in a photo, such as limbs and torso, with a DeepLabV3+ model from Keras in a Gradio page, with example images. For learners and computer vision developers.

Made by keras-io 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: Human Part Segmentation is a Gradio app that marks the body parts of a person in a photo, by its name. The record names one Keras model, a DeepLabV3+ model with a ResNet50 backbone. Example images are included, and the Space runs on upgraded CPU hardware at Hugging Face.

Who it suits: It suits computer vision learners and developers who want to see segmentation on people, and designers who want a quick look at how a model splits a person into parts. Use only photos you have the right to use.

Industry | Who uses it | How they use it | Example
Education | Computer vision students | See how a segmentation model labels each part of a person | Run an example image and view the colored parts
Design | Creative technologists | Check a model's body part masks as a starting point for a project | Try a photo you own
Software | Python developers | Read a small Gradio demo around a Keras model | Open app.py and requirements.txt

You can skip it if your photos contain no people.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Windows, macOS or Linux with Python, from a downloaded copy

Use case | Who | What they get back
Segment a person in a photo | Learners and developers | An image with body parts marked
Try the bundled examples | Anyone | A quick sense of what the model does
Reuse as a base | Developers | A small Gradio app around a Keras model

How to set it up:

• Nothing at all if you use the online Space
• Python on your computer, only if you run a copy
• The packages listed in requirements.txt

1. Open the Space's page in a browser to use it with nothing to install.
2. To run a copy, install the Hugging Face Hub package and download the Space.
3. Move into the new folder, then install Gradio and the listed requirements.
4. Start the app with Python.

pip install -U huggingface_hub
hf download keras-io/Human-Part-Segmentation --repo-type space --local-dir Human-Part-Segmentation
cd Human-Part-Segmentation
pip install gradio
pip install -r requirements.txt
python app.py

How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The example images are the quickest way to try it.

python app.py

Example: You open the Space and pick one of the example photos of a person. The app returns the photo with the person's body parts marked. The record does not describe the output, so the exact display depends on the page.

For newcomers and experts: Segmentation means a program labels every pixel of an image, here by the part of the body it belongs to. Start by opening the Space and running one example image.

If you know the field, the model pairs DeepLabV3+ with a ResNet50 backbone, a common design for pixel-level labels. The record gives no class list, resolution limits or accuracy figures, so check results on your own images.

You pick or upload a photo
The Gradio page sends it to app.py
The DeepLabV3+ model labels each pixel
The page shows the marked parts

Good to know:

• License: cc0-1, as listed on the Space
• Cost to run: none named
• Data it reaches: the photos you give it and the model it names
• Not for: the record names no exclusions, so use only photos you have the right to use

On Hugging Face, 13 November 2026

• Likes: 36
• Built with: gradio
• License: cc0-1.0

Links

• Space: https://huggingface.co/spaces/keras-io/Human-Part-Segmentation
• Files: https://huggingface.co/spaces/keras-io/Human-Part-Segmentation/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.