README.md

metadata

task_categories:
  - object-detection
tags:
  - yolo
  - yolo11
  - hardhat
  - hat
base_model:
  - Ultralytics/YOLO11
widget:
  - text: Helmet detection
    output:
      url: example.png
pipeline_tag: object-detection
model-index:
  - name: hardhat-or-hat
    results:
      - task:
          type: object-detection
        dataset:
          type: safety-equipment
          name: Safety Equipment
          args:
            epochs: 65
            batch: 16
            imgsz: 640
            patience: 15
            optimizer: SGD
            lr0: 0.001
            lrf: 0.01
            momentum: 0.9
            weight_decay: 0.0005
            warmup_epochs: 3
            warmup_bias_lr: 0.01
            warmup_momentum: 0.8
        metrics:
          - type: precision
            name: Precision
            value: 0.89279
          - type: recall
            name: Recall
            value: 0.89279
          - type: mAP50
            name: mAP50
            value: 0.56717
          - type: mAP50-95
            name: mAP50-95
            value: 0.56717

Model for detecting Hardhats and Hats

Model binary

You can download:

• v1 that only detects Hardhat or no-hardhat
• v2 that detects Hardhat, no-hardhat or Hat

Base Model

Ultralytics/YOLO11m

Huggingface page

https://huggingface.co/luisarizmendi/hardhat-or-hat

Model Dataset

https://universe.roboflow.com/luisarizmendi/hardhat-or-hat

Labels

- hat
- helmet
- no_helmet

Model metrics

Model training

You can review the Jupyter notebook here

Hyperparameters

base model: yolov11x.pt
epochs: 150
batch: 16
imgsz: 640
patience: 15
optimizer: 'SGD'
lr0: 0.001
lrf: 0.01
momentum: 0.9
weight_decay: 0.0005
warmup_epochs: 3
warmup_bias_lr: 0.01
warmup_momentum: 0.8

Model Usage

Usage with Huggingface spaces

If you don't want to run it locally, you can use this huggingface space that I've created with this code but be aware that this will be slow since I'm using a free instance, so it's better to run it locally with the python script below.

Remember to check that the Model URL is pointing to the model that you want to test.

Usage with Python script

Install the following PIP requirements

gradio
ultralytics
Pillow
opencv-python
torch

Then run the python code below and open http://localhost:8800 in a browser to upload and scan the images.

import gradio as gr
from ultralytics import YOLO
from PIL import Image
import os
import cv2
import torch

DEFAULT_MODEL_URL = "https://huggingface.co/luisarizmendi/hardhat-or-hat/tree/main/v2/model/pytorch/best.pt"

def detect_objects_in_files(model_input, files):
    """
    Processes uploaded images for object detection.
    """
    if not files:
        return "No files uploaded.", []

    model = YOLO(str(model_input))
    if torch.cuda.is_available():
        model.to('cuda')
        print("Using GPU for inference")
    else:
        print("Using CPU for inference")

    results_images = []
    for file in files:
        try:
            image = Image.open(file).convert("RGB")
            results = model(image)
            result_img_bgr = results[0].plot()
            result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB)
            results_images.append(result_img_rgb)

            # If you want that images appear one by one (slower)
            #yield "Processing image...", results_images

        except Exception as e:
            return f"Error processing file: {file}. Exception: {str(e)}", []

    del model
    torch.cuda.empty_cache()

    return "Processing completed.", results_images

interface = gr.Interface(
    fn=detect_objects_in_files,
    inputs=[
        gr.Textbox(value=DEFAULT_MODEL_URL, label="Model URL", placeholder="Enter the model URL"),
        gr.Files(file_types=["image"], label="Select Images"),
    ],
    outputs=[
        gr.Textbox(label="Status"),
        gr.Gallery(label="Results")
    ],
    title="Object Detection on Images",
    description="Upload images to perform object detection. The model will process each image and display the results."
)

if __name__ == "__main__":
    interface.launch()