![]() ![]() Use dropout regularization (classify train only) Mask downsample ratio (segment train only) Masks should overlap during training (segment train only) Profile ONNX and TensorRT speeds during training for loggers (int) disable mosaic augmentation for final epochsĪutomatic Mixed Precision (AMP) training, choices=ĭataset fraction to train on (default is 1.0, all images in train set) Rectangular training with each batch collated for minimum padding Number of worker threads for data loading (per RANK if DDP) cuda device=0 or device=0,1,2,3 or device=cpu Use cache for data loadingĭevice to run on, i.e. Save checkpoint every x epochs (disabled if < 1) Save train checkpoints and predict results Number of images per batch (-1 for AutoBatch) yolov8n.pt, yolov8n.yamlĮpochs to wait for no observable improvement for early stopping of training Careful tuning and experimentation with these settings are crucial for optimizing performance. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Key training settings include batch size, learning rate, momentum, and weight decay. These settings influence the model's performance, speed, and accuracy. The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. train, val, predict, export, track, benchmark Benchmark: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy. ![]() Track: For tracking objects in real-time using a YOLOv8 model. ![]() Export: For exporting a YOLOv8 model to a format that can be used for deployment. Predict: For making predictions using a trained YOLOv8 model on new images or videos. Val: For validating a YOLOv8 model after it has been trained. Train: For training a YOLOv8 model on a custom dataset. YOLO models can be used in different modes depending on the specific problem you are trying to solve. Pose: For identifying objects and estimating their keypoints in an image or video. Classify: For predicting the class label of an input image. Segment: For dividing an image or video into regions or pixels that correspond to different objects or classes. These tasksĭiffer in the type of output they produce and the specific problem they are designed to solve.ĭetect: For identifying and localizing objects or regions of interest in an image or video. YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose.
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