diff --git a/README.md b/README.md
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+# Yet another custom node to detect human parts
+
+Detect human parts using the DeepLabV3+ ResNet50 model from Keras-io. You can extract hair, arms, legs, and other parts
+with ease and with small memory usage.
+
+This node aims to detect human parts using the model created by
+[Keras-io](https://huggingface.co/keras-io/deeplabv3p-resnet50). Their "[Space](https://huggingface.co/spaces/keras-io/Human-Part-Segmentation)" was impressive, and I wanted to use the
+model.
+
+Unfortunately, the model uses an old Keras version, and there were no PyTorch implementation.
+
+So I decided to convert the model to [ONNX](https://onnx.ai/) format and to create my [HugginFace
+repository](https://huggingface.co/Metal3d/deeplabv3p-resnet50-human) to share the model with the community.
+
+> Fortunately, Keras provides the model with a CC1.0 license, thank you guys to allow us to use it without any
+> restriction.
+
+## Example
+
+You can drag and drop the following image to try:
+
+![Example workflow](./images/Human Parts.png)
+
+## DeepLabV3+ ResNet50 for Human
+
+Actually, all the model I found was not trained to detect human parts, but to detect some objects or urban elements. The
+Keras model is the only one I found that works!
+
+## Installation
+
+I strongly recommend to use ComfyUI-Manager to install the node. It will install the dependencies and the model.
+
+If you're using the command line, you can install the node with:
+
+```bash
+cd /path/to/your/ComfyUI/custom_nodes
+git clone ...
+cd HumanParts
+pip install -r requirements.txt
+# or
+python -m pip install -r requirements.txt
+```
+
+Then, restart ComfyUI and you may find the "HumanParts" node.
+
+![The node](./images/node.png)
diff --git a/__init__.py b/__init__.py
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+__all__ = ["HumanParts"]
+
+from .nodes import HumanParts
+
+NODE_CLASS_MAPPINGS = {
+    "HumanParts": HumanParts,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HumanParts": "🧍 Human Parts mask generator",
+}
diff --git a/detector/__init__.py b/detector/__init__.py
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diff --git a/detector/human_parts.py b/detector/human_parts.py
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+++ b/detector/human_parts.py
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+from typing import Tuple
+
+import numpy as np
+import torch
+from onnxruntime import InferenceSession
+from PIL import Image
+
+# classes used in the model
+classes = {
+    "background": 0,
+    "hair": 2,
+    "glasses": 4,
+    "top-clothes": 5,
+    "bottom-clothes": 9,
+    "torso-skin": 10,
+    "face": 13,
+    "left-arm": 14,
+    "right-arm": 15,
+    "left-leg": 16,
+    "right-leg": 17,
+    "left-foot": 18,
+    "right-foot": 19,
+}
+
+
+def get_mask(
+    image: torch.Tensor, model: InferenceSession, rotation: float, **kwargs
+) -> Tuple[torch.Tensor, int]:
+    """
+    Return a Tensor with the mask of the human parts in the image.
+
+    The rotation parameter is not used for now. The idea is to propose rotation to help
+    the model to detect the human parts in the image if the character is not in a casual position.
+    Several tests have been done, but the model seems to fail to detect the human parts in these cases,
+    and the rotation does not help.
+    """
+
+    image = image.squeeze(0)
+    image_np = image.numpy() * 255
+
+    pil_image = Image.fromarray(image_np.astype(np.uint8))
+    original_size = pil_image.size  # to resize the mask later
+    # resize to 512x512 as the model expects
+    pil_image = pil_image.resize((512, 512))
+    center = (256, 256)
+
+    if rotation != 0:
+        pil_image = pil_image.rotate(rotation, center=center)
+
+    # normalize the image
+    image_np = np.array(pil_image).astype(np.float32) / 127.5 - 1
+    image_np = np.expand_dims(image_np, axis=0)
+
+    # use the onnx model to get the mask
+    input_name = model.get_inputs()[0].name
+    output_name = model.get_outputs()[0].name
+    result = model.run([output_name], {input_name: image_np})
+    result = np.array(result[0]).argmax(axis=3).squeeze(0)
+
+    score: int = 0
+
+    mask = np.zeros_like(result)
+    for class_name, enabled in kwargs.items():
+        if enabled and class_name in classes:
+            class_index = classes[class_name]
+            detected = result == class_index
+            mask[detected] = 255
+            score += mask.sum()
+
+    # back to the original size
+    mask_image = Image.fromarray(mask.astype(np.uint8), mode="L")
+    if rotation != 0:
+        mask_image = mask_image.rotate(-rotation, center=center)
+
+    mask_image = mask_image.resize(original_size)
+
+    # and back to numpy...
+    mask = np.array(mask_image).astype(np.float32) / 255
+
+    # add 2 dimensions to match the expected output
+    mask = np.expand_dims(mask, axis=0)
+    mask = np.expand_dims(mask, axis=0)
+    # ensure to return a "binary mask_image"
+
+    del image_np, result  # free up memory, maybe not necessary
+    return (torch.from_numpy(mask.astype(np.uint8)), score)
diff --git a/images/Human Parts.png b/images/Human Parts.png
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diff --git a/images/node.png b/images/node.png
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diff --git a/install.py b/install.py
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+++ b/install.py
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+import os
+import urllib.request
+
+from tqdm import tqdm
+
+try:
+    from .utils import model_name, model_path, model_url, models_dir_path
+except ImportError:
+    from utils import model_name, model_path, model_url, models_dir_path
+
+
+def download(url, path, name):
+    request = urllib.request.urlopen(url)
+    total = int(request.headers.get("Content-Length", 0))
+    with tqdm(
+        total=total,
+        desc=f"[HumanParts] Downloading {name} to {path}",
+        unit="B",
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as progress:
+        urllib.request.urlretrieve(
+            url,
+            path,
+            reporthook=lambda count, block_size, total_size: progress.update(
+                block_size
+            ),
+        )
+
+
+if not os.path.exists(models_dir_path):
+    os.makedirs(models_dir_path)
+
+if not os.path.exists(model_path):
+    download(model_url, model_path, model_name)
diff --git a/nodes.py b/nodes.py
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+++ b/nodes.py
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+from typing import Tuple
+
+import onnxruntime as ort
+import torch
+
+from .utils import model_path
+from .detector.human_parts import get_mask
+
+
+class HumanParts:
+    """
+    This node is used to get a mask of the human parts in the image.
+
+    The model used is DeepLabV3+ with a ResNet50 backbone trained
+    by Keras-io, converted to ONNX format.
+
+    """
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "get_mask"
+    CATEGORY = "Metal3d"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        def _bool_widget(is_enabled=False, tooltip: str | None = None):
+            """Helper function to create a boolean widget"""
+            return (
+                "BOOLEAN",
+                {
+                    "default": is_enabled,
+                    "label_on": "Enabled",
+                    "label_off": "Disabled",
+                    "tooltip": tooltip,
+                },
+            )
+
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "background": _bool_widget(
+                    tooltip="Background, excluding human parts, invert this mask to get the human parts",
+                ),
+                "face": _bool_widget(
+                    is_enabled=True,
+                    tooltip="Face, including eyes, mouth, etc.",
+                ),
+                "hair": _bool_widget(
+                    tooltip="Hair, including beard, mustache, etc.",
+                ),
+                "glasses": _bool_widget(
+                    tooltip="Glasses, sunglasses, etc. Eyes can be included"
+                ),
+                "top-clothes": _bool_widget(
+                    tooltip="Shirt, T-shirt, etc.",
+                ),
+                "bottom-clothes": _bool_widget(
+                    tooltip="Pants, shorts, etc.",
+                ),
+                "torso-skin": _bool_widget(
+                    tooltip="Skin of the torso, excluding clothes. Neck can be included"
+                ),
+                "left-arm": _bool_widget(
+                    tooltip="Left arm, excluding clothes, hand can be included"
+                ),
+                "right-arm": _bool_widget(
+                    tooltip="Right arm, excluding clothes, hand can be included"
+                ),
+                "left-leg": _bool_widget(
+                    tooltip="Left leg, excluding clothes, foot can be included"
+                ),
+                "right-leg": _bool_widget(
+                    tooltip="Right leg, excluding clothes, foot can be included"
+                ),
+                "left-foot": _bool_widget(
+                    tooltip="Left foot, excluding shoes",
+                ),
+                "right-foot": _bool_widget(
+                    tooltip="Right foot, excluding shoes",
+                ),
+            }
+        }
+
+    def get_mask(self, image: torch.Tensor, **kwargs) -> Tuple[torch.Tensor]:
+        """
+        Return a Tensor with the mask of the human parts in the image.
+        """
+
+        model = ort.InferenceSession(model_path)
+        ret_tensor, _ = get_mask(image, model=model, rotation=0, **kwargs)
+
+        return (ret_tensor,)
diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+onnxruntime
+numpy
+Pillow
diff --git a/utils.py b/utils.py
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+++ b/utils.py
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+import os
+
+# get the model paths
+try:
+    from folder_paths import models_dir  # pyright: ignore
+except ImportError:
+    from pathlib import Path
+
+    models_dir = os.path.join(Path(__file__).parents[2], "models")
+
+models_dir_path = os.path.join(models_dir, "onnx", "human-parts")
+model_url = "https://huggingface.co/Metal3d/deeplabv3p-resnet50-human/resolve/main/deeplabv3p-resnet50-human.onnx"
+model_name = os.path.basename(model_url)
+model_path = os.path.join(models_dir_path, "deeplabv3p-resnet50-human.onnx")