How to Train a ResNet-34 Model on a Custom Dataset

ResNet-34, introduced in 2015, is an efficient image classification model that can be trained to categorize image contents, making it ideal for tasks requiring fast classification, such as quality assurance in manufacturing. This guide demonstrates how to train a ResNet-34 model using Roboflow to classify defects in juice boxes, identifying issues like loose straws or broken wrappers. The process involves preparing and annotating a dataset, generating a dataset version, and training the model with specific configurations. The trained model is then deployed using Roboflow Inference, allowing users to run custom workflows on their hardware. The guide emphasizes the flexibility of using the Roboflow platform for dataset management and model deployment, encouraging experimentation with different workflow blocks to enhance model utility.