How to Train a ResNet-18 Model with a Custom Dataset

ResNet-18 is a compact and efficient supervised classification model that leverages the ResNet convolutional neural network architecture, consisting of 18 convolutional and residual layers, making it suitable for edge deployment at high frame rates. The process of training a ResNet-18 model in the cloud involves using Roboflow Train, a platform that facilitates the preparation and annotation of datasets, such as labeling images of metal plates for defect classification. Once labeled, the dataset undergoes preprocessing steps before model training commences using the ResNet-18 architecture. After training, the model can be deployed on personal hardware through Roboflow Workflows and Inference, allowing users to create custom logic for model deployment. This guide exemplifies the workflow by successfully training a ResNet-18 model to identify defects in metal plates, demonstrating the model's deployment capabilities using a sample image to confirm its functionality.

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