1.2.0

[1.2.0] - 2024-03-05

Changed

• Creating code to work with Ultralytics YoloV8 Detection model (training and inference)
• Updated previous tutorials to work with the latest mltu changes
• Updated mltu.augmentors.RandomRotate to work with Detections objects
• Changed to use importlib to import librosa in mltu.preprocessors to avoid import errors
• Changed mltu.torch.model.Model object to provide more flexibility in training and validation
• Improved mltu.torch.callbacks to provide more flexibility in training and validation

Added

• Added mltu.torch.detection module, that contains Detections and Detection objects, to handle detection annotations
• Added RandomDropBlock and RandomDropBlock augmentors into mltu.augmentors to work with Detections objects
• Added ModelEMA into mltu.torch.model to work with EMA (Exponential Moving Average) model
• Added FpsWrapper into mltu.inferenceModel to automatically calculate FPS (Frames Per Second) when using inference model
• Added mltu.torch.yolo.detector.BaseDetector as a base class for preprocessing and postprocessing detection models
• Added mltu.torch.yolo.detector.onnx_detector.Detector as a class to handle YoloV8 onnx model detection inference
• Added mltu.torch.yolo.detector.torch_detector.Detector as a class to handle YoloV8 torch model detection inference
• Added mltu.torch.yolo.loss.v8DetectionLoss as a class to handle YoloV8 detection loss in training
• Added mltu.torch.yolo.metrics.YoloMetrics as a class to handle YoloV8 detection metrics in training and validation
• Added mltu.torch.yolo.optimizer module, that contains AccumulativeOptimizer object and build_optimizer function, to handle YoloV8 detection optimizer in training
• Added YoloV8 Detection tutorial in Tutorials.11_yolov8 that shows how to do basic inference with torch and exported onnx models