Using YOLOv8 for Object Classification
Last updated September 4, 2024
YOLOv8, a versatile object detection framework, can be leveraged beyond simply identifying objects in images. It can also classify objects based on their attributes, categorizing them into distinct classes. This guide explores how to implement object classification using YOLOv8, enhancing its functionality for richer applications.
Adapting YOLOv8 for Object Classification
- Dataset Preparation: Gather a dataset of images containing the objects you want to classify. Annotate these images with bounding boxes around each object and assign a specific class label to each box.
- Model Selection: Choose the appropriate YOLOv8 model variant:
- YOLOv8n: A smaller model, prioritizing speed.
- YOLOv8s: A medium-sized model, balancing speed and accuracy.
- YOLOv8m: A larger model, focusing on accuracy.
- YOLOv8l: The largest model, striving for the highest accuracy.
- Training Configuration: Configure training parameters in a `train.yaml` file, setting options like batch size, epochs, learning rate, and data augmentation techniques to optimize for classification.
- Training Execution: Initiate training using the Ultralytics library's command-line interface: `python train.py --data your_data.yaml --cfg your_model.yaml`.
- Evaluation: Evaluate your trained model's performance on a validation set using metrics like classification accuracy, precision, recall, and F1-score.
Performing Inference for Object Classification
- Model Loading: Load the trained YOLOv8 model using the Ultralytics library: `model = torch.hub.load('ultralytics/yolov8', 'your_trained_model')`.
- Input Processing: Prepare input images in the required format for the model.
- Inference Execution: Run inference on your input data: `results = model(image)`.
- Result Interpretation: Extract the predicted class labels and confidence scores for each detected object from the `results` object.
- Visualization: Display the detected objects with their associated class labels on the input images using the Ultralytics library's visualization functions.
Applications of YOLOv8 Object Classification
- Image Analysis: Classify objects within images for tasks like image tagging, content categorization, and semantic understanding.
- Retail Analytics: Analyze store surveillance footage to identify customer demographics, product preferences, and shopping behaviors.
- Wildlife Monitoring: Classify animal species in photos or videos for research, conservation, and ecological monitoring.
- Medical Diagnosis: Classify medical images like X-rays or CT scans to assist in diagnosing conditions and identifying abnormalities.
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