2 Repos
PyTorch implementations of the EfficientNet family of convolutional neural networks.
Distinct from PyTorch Training Frameworks: The candidate tags are too generic (PyTorch frameworks) or too narrow (normalizing flows).
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This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model library providing architectures for image classification and high-level feature extraction, including pre-trained weights for immediate image categorization. The library supports transfer learning by allowing the modification of model architectures and output layers to accommodate a custom number of classes for new datasets. It also includes a model exporter to convert trained PyTorch weights into the ONNX format for production inference. The system covers broader computer vis
Provides a full PyTorch implementation of EfficientNet convolutional neural networks.
tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det
Executes EfficientNet model predictions by converting weights into a serialized hardware engine.