All available versions of YOLOv8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios, crucial for comprehensive agricultural monitoring. The ‘large’ version (YOLOv8l) wa
Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Question I have seen that Ultralytics YOLOv8 incorporates Ray Tune for hyperparameter tuning. Is there a way I can add model architect...
Reducing MLP dimensions and hidden dimensions can help manage computational resources without significantly sacrificing performance, highlighting the importance of careful hyperparameter tuning in ViT models. 3.4. Analysis of ViT performance through hyperparameter combinations In the present study, Sankey ...
In the above code snippet, a detection_model has been initialized. In this experiment,‘model_type’is yolov8,‘model_path’points to the directory where the model has been saved, and the default ‘confidence_threshold’ has been set to 0.2. If you have a machine with NVIDIA GPUs, you c...
Objectives: To enhance tree detection in static images by comparing the performance of YOLOv5, YOLOv8, and YOLOv11 models. The comparison involved hyperparameter tuning and the application of various optimizers, aiming to improve model performance in terms of precision, reca...
This study investigates the performance of YOLOv8, a Convolutional Neural Network (CNN) architecture, for multi-crop classification in a mixed farm with Unmanned Aerial Vehicle (UAV) imageries. Emphasizing hyperparameter optimization, specifically batch size, the study's primary objective is to refine...
3.2. Hyperparameter Optimization YOLOv11m was selected for the hyperparameter optimization during fine-tuning. Two types of algorithms, OFAT and RS, were used consecutively for hyperparameter optimization to improve the efficiency and accuracy of the tomato leaf disease recognition model. The most cr...
In recent studies, hyperparameter optimization has been extensively applied to improve the performance of deep learning models in agriculture. For example, Ramos et al. optimized the YOLOv8 model for smoke and wildfire detection to enhance agricultural safety [41]. By utilizing a one-factor-at-a...