Getting Started with YOLO AI for Image Detection

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  • #760
    Jack
    Moderator

    Image detection has revolutionized many aspects of technology, from self-driving cars to surveillance systems. One of the most accessible yet powerful tools in image detection technology is YOLO (You Only Look Once). This blog post will guide you through downloading and installing YOLO, using it for image detection, and training a model with a custom dataset. We’ll also discuss using a Docker instance of MakeSense for labelling your images.

    1. Downloading and Installing YOLO
    YOLO is available in several versions, with YOLOv5 being one of the most popular due to its balance of speed and accuracy. To get started, you’ll need Python installed on your system, along with PyTorch, as YOLOv5 relies on this framework. Below are the steps to follow to install YOLO.
    Install Python: Ensure Python 3.6 or higher is installed on your system. You can download it from the official Python website.
    Install PyTorch: Visit the PyTorch official website and follow the instructions to install it based on your operating system and preferences for CUDA support.
    Clone YOLOv5 Repository:
    Open a terminal and run the following command:
    git clone https://github.com/ultralytics/yolov5
    cd yolov5
    Install Requirements:
    Within the YOLOv5 directory, install the required libraries using pip:
    pip install -r requirements.txt
    With these steps, you’ve successfully installed YOLOv5 on your machine and are ready to start using it for image detection.

    2. Using YOLO for Image Detection
    YOLO can perform detection tasks directly from the command line using pre-trained models. Here’s how you can use it:
    Detecting Images:
    To detect objects in images, use the following command:
    python detect.py –source path_to_image_or_folder –weights yolov5s.pt –conf 0.4
    –source: Path to your image or directory of images.
    –weights: Specifies the model weights file. yolov5s.pt is the default small model for faster inference.
    –conf: Confidence threshold to filter predictions.
    This command will process the images and save the results in the runs/detect directory inside the YOLOv5 folder.

    3. Training Your Own Model
    Training your model with YOLOv5 involves creating a custom dataset, which you can efficiently perform using MakeSense.ai in a Docker environment.
    Creating a Dataset:
    Setting Up MakeSense with Docker:
    First, ensure Docker is installed on your machine. If not, download it from the Docker website.
    Pull and run the MakeSense Docker image:
    docker pull makesenseai/makesense:latest
    docker run -p 8501:8501 makesenseai/makesense:latest
    Open your web browser and go to http://localhost:8501 to start using MakeSense.
    Labelling Images:
    Upload your images to MakeSense and start labelling them according to the classes of objects you want YOLO to detect.
    Export the labelled data in YOLO format, which includes image files and corresponding annotation files.
    Training the Model:
    Once your dataset is ready, you can train your model using the following command:
    python train.py –img 640 –batch 16 –epochs 50 –data custom_dataset.yaml –weights yolov5s.pt
    –img: Defines the size of the images.
    –batch: Batch size during training.
    –epochs: Number of training epochs.
    –data: Path to a YAML file describing your dataset.
    –weights: Starting weights; use a pre-trained model for transfer learning.

    This is just the start of your AI image analysis journey, this can be combined with other python scripts to perform analysis on real-time web footage, IP cameras, or even images scraped from social media. Good luck!

    #762
    Andy
    Keymaster

    A great post Jack, we look forward to many more on this topic.

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