Title: Launching ResNet18-Based Weeds Detection for Enhanced Agricultural Insight
Key Features:

WeedsDetection Module: This core component leverages a custom-trained ResNet18 model for the identification and classification of 27 distinct weed species within agricultural imagery. The module is engineered to deliver annotated images that not only pinpoint the location of weeds but also identify their species with remarkable accuracy.
Data Augmentation Techniques: Recognizing the variable conditions of agricultural environments, the model incorporates advanced data augmentation strategies—including random flips, rotations, and color adjustments. These techniques significantly enhance the model's adaptability and performance across diverse scenarios.
Efficient Training Protocol with EarlyStopping: To ensure optimal training outcomes and prevent overfitting, an EarlyStopping mechanism has been implemented. This approach meticulously balances model training to achieve the best possible performance without compromising generalizability.
Rigorous Validation Approach: Employing a 2-fold stratified cross-validation method, the model's reliability and accuracy are extensively evaluated. This rigorous validation ensures the model's robust performance across a wide range of data sets, affirming its utility in practical applications.