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README.dataset.txt

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+# Helmet & No helmet detection > 2025-12-20 5:14pm
+https://universe.roboflow.com/saad-n/helmet-no-helmet-detection-f1heo
+
+Provided by a Roboflow user
+License: MIT
+

README.roboflow.txt

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+
+Helmet & No helmet detection - v1 2025-12-20 5:14pm
+==============================
+
+This dataset was exported via roboflow.com on December 20, 2025 at 12:14 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 984 images.
+-Helmet-Bald-Hair-Cap are annotated in YOLOv8 format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e321aea22208bfa37b73649dc5c5d1e2d855c140c5055ee002859975a7cc192
+size 18461987

data.yaml

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+train: train/images
+val: valid/images
+test: test/images
+
+nc: 6
+names: ['1-2-helmet', '3-4-helmet', 'Bald', 'Cap', 'Face and Hair', 'Full-face-helmet']

detect_helmet.py

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+import cv2
+from ultralytics import YOLO
+import os
+
+# --- Path Configuration ---
+# Training khatam hone par model 'train2' folder mein banega
+BEST_MODEL_PATH = 'best.pt'
+
+
+# 1. Load your custom trained model
+if not os.path.exists(BEST_MODEL_PATH):
+    print(f"Waiting for model... File not found at {BEST_MODEL_PATH}")
+    print("Training khatam hone ka intezar karein.")
+else:
+    model = YOLO(BEST_MODEL_PATH)
+
+    # Categories based on your data.yaml
+    SAFE_CLASSES = ['1-2-helmet', '3-4-helmet', 'Full-face-helmet']
+    WARNING_CLASSES = ['Bald', 'Cap', 'Face and Hair']
+
+    cap = cv2.VideoCapture(0) # Webcam open karein
+
+    print("System Started. Press 'q' to quit.")
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret: break
+
+        # Detection shuru
+        results = model.predict(source=frame, conf=0.50, stream=True)
+
+        for result in results:
+            names = result.names 
+            for box in result.boxes:
+                class_id = int(box.cls[0])
+                conf = float(box.conf[0])
+                label_name = names[class_id]
+                
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+
+                # Color logic
+                if label_name in SAFE_CLASSES:
+                    color = (0, 255, 0) # Green for SAFE
+                    msg = f"SAFE: {label_name} {conf:.2f}"
+                elif label_name in WARNING_CLASSES:
+                    color = (0, 0, 255) # Red for WARNING
+                    msg = f"WARNING: No Helmet ({label_name}) {conf:.2f}"
+                else:
+                    color = (255, 255, 255) # White for others
+                    msg = f"{label_name} {conf:.2f}"
+
+                cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3)
+                cv2.putText(frame, msg, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+        cv2.imshow("Helmet Detection Final Assignment", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'): break
+
+    cap.release()
+    cv2.destroyAllWindows()

test_image.py

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+# test_image.py
+from ultralytics import YOLO
+import cv2
+import os
+
+# Training ke baad wala model path
+model_path = 'runs/detect/train2/weights/best.pt'
+
+if os.path.exists(model_path):
+    model = YOLO(model_path)
+    
+    # Dataset ke test folder mein se kisi image ka path
+    # Aap manually koi bhi image ka path yahan paste kar sakte hain
+    img_path = input("Enter image path (e.g., test/images/1.jpg): ")
+
+    if os.path.exists(img_path):
+        results = model(img_path)
+        
+        for r in results:
+            im_array = r.plot()  # Detection draw karega
+            cv2.imshow('Final Test', im_array)
+            cv2.waitKey(0) # Press any key to close
+            cv2.destroyAllWindows()
+    else:
+        print("Image file nahi mili! Path check karein.")
+else:
+    print("Model abhi tak train nahi hua. Training khatam hone ka intezar karein.")

train_model.py

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+from ultralytics import YOLO
+import os
+
+# --- Configurations ---
+DATA_YAML_PATH = 'data.yaml'
+# Ye path confirm kar lein ke aapke folder structure ke mutabiq sahi ho.
+# Maslan agar Helmet - No helmet detection.v1i.yolov8 folder C drive mein hai, 
+# toh uska pura path de sakte hain: 'C:/Users/YourName/.../data.yaml'
+
+EPOCHS = 50 # Behtar accuracy ke liye 50 epochs
+
+# 1. Load a pretrained YOLOv8n model
+model = YOLO('yolov8n.pt') 
+
+# 2. Train the model
+print(f"Starting training with data from: {DATA_YAML_PATH}")
+if not os.path.exists(DATA_YAML_PATH):
+    print(f"ERROR: data.yaml file not found at {DATA_YAML_PATH}")
+    print("Please make sure the path is correct and the file exists.")
+else:
+    results = model.train(
+        data=DATA_YAML_PATH,  
+        epochs=EPOCHS,        
+        imgsz=640,            
+        plots=True            
+    )
+    print("Training completed! Check 'runs/detect/train/weights/best.pt' for your trained model.")

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796