Object Detection Using TensorFlow 2 and Amazon SageMaker

Many frameworks with packaged pre-guided models have been created to give users fast access to transfer learning considering the rapidly expanding field of object detection techniques. For instance, three well-known computer vision systems with trained models are GluonCV, Detectron2, and the Object Detection API for TensorFlow, as well. The TensorFlow2 Object Detection API is an update to the TensorFlow Object Detection API. One of the cutting-edge object identification algorithms that can be trained using the TensorFlow2 Object Detection API is EfficientDet from Google Brain (Implemented here). Authors created and implemented an EfficientDet model with the help of TensorFlow Object Detection API using Amazon SageMaker. It is constructed on top of TensorFlow 2, which facilitates its creation, training, and deployment. Because it is designed on top of TensorFlow 2, creating, training, and deploying object detection models is simple. SageMaker is a completely managed tool that lets data scientists and developers quickly build, direct, and implement ML models. To make it simpler to create high-quality models, SageMaker takes the labour-intensive tasks out of each stage of the ML process. Transfer learning on numerous pre-guided models accessible in TensorFlow Hub is made possible by object detection with TensorFlow in SageMaker. The head of the TensorFlow model that handles object detection is replaced based on the amount of class labels present in the guiding data. Based on fresh guiding data, either the entire network—including pre-guided model—or just the top layer (object recognition head) can be fine-tuned. Authors trained using a smaller dataset in this transfer learning method. Authors have talked over each step-in detail, including data collection and labelling with Ground Truth, making, and converting the data to TFRecord format, training as well as launching a special object detection model with the TensorFlow Object Detection API, and ultimately deploying the model.