Create README.md

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- yolo
+- yolo11
+- hardhat
+- hat
+datasets:
+- luisarizmendi/safety-equipment
+base_model:
+- Ultralytics/YOLO11
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
+  example_title: Football Match
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
+  example_title: Airport
+pipeline_tag: object-detection
+model-index:
+- name: yolo11-safety-equipment
+  results:
+  - task:
+      type: object-detection
+    dataset:
+      type: safety-equipment
+      name: Safety Equipment
+      args:
+        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
+    metrics:
+    - type: precision
+      name: Precision
+      value: 0.9078
+    - type: recall
+      name: Recall
+      value: 0.9064
+    - type: mAP50
+      name: mAP50
+      value: 0.9589
+    - type: mAP50-95
+      name: mAP50-95
+      value: 0.6088
+---
+
+# Model for detecting Hardhats and Hats
+
+
+<div align="center">
+  <img width="640" alt="luisarizmendi/safety-equipment" src="https://huggingface.co/luisarizmendi/hardhat-or-hat/resolve/main/example.png">
+</div>
+
+## Model binary
+
+You can [download the model from here](https://github.com/luisarizmendi/ai-apps/raw/refs/heads/main/models/luisarizmendi/object-detector-safety/object-detector-safety-v1.pt)
+
+
+## Labels
+
+```
+- hat
+- helmet
+- no_helmet
+```
+
+
+## Model metrics
+
+
+<div align="center">
+  <img width="640" alt="luisarizmendi/safety-equipment" src="https://huggingface.co/luisarizmendi/yolo11-safety-equipment/resolve/main/confusion_matrix_normalized.png"> <img width="640" alt="luisarizmendi/safety-equipment" src="https://huggingface.co/luisarizmendi/yolo11-safety-equipment/resolve/main/results.png">
+</div>
+
+
+## Model Dataset
+
+[https://universe.roboflow.com/luisarizmendi/hardhat-or-hat](https://universe.roboflow.com/luisarizmendi/hardhat-or-hat)
+
+
+
+## Model training
+
+### Notebook
+
+You can [review the Jupyter notebook here](https://huggingface.co/luisarizmendihardhat-or-hat/blob/main/train.ipynb)
+
+### 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](https://huggingface.co/spaces/luisarizmendi/safety-equipment-object-detection) 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.
+
+
+<div align="center">
+  <img width="640" alt="luisarizmendi/safety-equipment" src="https://huggingface.co/luisarizmendi/yolo11-safety-equipment/resolve/main/spaces-example.png">
+</div>
+
+
+### Usage with Python script
+
+Install the following PIP requirements
+
+```
+gradio
+ultralytics
+Pillow
+opencv-python
+torch
+```
+
+Then [run the python code below](https://huggingface.co/luisarizmendi/yolo11-safety-equipment/blob/main/run_model.py) 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()
+```