Model for safety-equipment detection

Model binary

Labels

- glove
- goggles
- helmet
- mask
- no_glove
- no_goggles
- no_helmet
- no_mask
- no_shoes
- shoes

Model metrics

Model Dataset

https://universe.roboflow.com/luisarizmendi/safety-or-hat/dataset/1

This dataset is based on this other one that you can find in Roboflow

Model training

Notebook

You can review the Jupyter notebook here

Hyperparameters

epochs: 35
batch: 2
imgsz: 640
patience: 5
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

Augmentation

hsv_h=0.015,  # Image HSV-Hue augmentationc
hsv_s=0.7,   # Image HSV-Saturation augmentation
hsv_v=0.4,   # Image HSV-Value augmentation
degrees=10,  # Image rotation (+/- deg)
translate=0.1,  # Image translation (+/- fraction)
scale=0.3,   # Image scale (+/- gain)
shear=0.0,   # Image shear (+/- deg)
perspective=0.0,  # Image perspective
flipud=0.1,  # Image flip up-down
fliplr=0.1,  # Image flip left-right
mosaic=1.0,  # Image mosaic
mixup=0.0,   # Image mixup

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.

Usage with Python script

Install the following PIP requirements

gradio
ultralytics
Pillow
opencv-python
torch

Then run the python code below and then open http://localhost:7860 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 

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

    device = "cuda" if torch.cuda.is_available() else "cpu"  
    model = YOLO("https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detector-safety/object-detector-safety-v1.pt")
    model.to(device)
    
    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.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()

luisarizmendi
/

yolo11-safety-equipment