这是Ultralytics基于yolov5的嗜睡检测模型

# Real-Time drowsiness Detection with YOLOv5 and PyTorch Identification of objects in an image considered a common assignment for the human brain, though not so trivial for a machine. Identification and localization of objects in photos is a computer vision task called ‘object detection’. One of the most popular algorithms to date for real-time object detection is YOLO (You Only Look Once). In this project, we performed drowsiness detection to check whether a person is awake or drowsy, using the latest YOLOv5 implementation developed by Ultralytics. ## Prerequisites - PyTorch - Torchvision - Torchaudio - OpenCV - Matplotlib - Numpy - ipywidgets - PyQt5 - lxml ## Installation 1. Install the required dependencies by running the following command: !pip3 install torch torchvision torchaudio !pip install opencv-python matplotlib numpy ipywidgets !pip install pyqt5==5.15.2 lxml 2. Clone the YOLOv5 repository by running: !git clone https://github.com/ultralytics/yolov5 3. Navigate to the cloned directory: cd yolov5 4. Install the requirements for YOLOv5: !pip install -r requirements.txt 5. Clone the labelImg repository for image labeling: !git clone https://github.com/tzutalin/labelImg.git ## Usage 1. Collect Images: - Connect a webcam to your computer. - Run the code to capture images for the specified labels ("awake" and "drowsy"). - Images will be saved in the "data/images" directory. 2. Label Images: - Open the labelImg tool to label the collected images as "awake" or "drowsy". - Save the labeled annotations as XML files. 3. Train the YOLOv5 Model: - Run the training script with the specified parameters (image size, batch size, epochs, data configuration, weights, etc.). - The model will be trained on the labeled images using the YOLOv5 architecture. 4. Load the Trained Model: - Load the trained model weights for inference. - Perform object detection on images or video streams.