#EfficientNet

QuickAI facilitates testing of complex Machine Learning models with minimal Python code. It supports diverse architectures like EfficientNet, VGG, ResNet, YOLO, and GPT-NEO for tasks such as image classification, NLP, and object detection. Compatible with TensorFlow and PyTorch, and offering Docker for easy setup, it simplifies development by reducing code length and enhancing productivity.

Discover TensorFlow's diverse pretrained image models, including Vision Transformers and ResNets with ImageNet weights. Easily create and customize models beyond classification using advanced features like Segment Anything. Compatible with Keras, installation is straightforward, leveraging model versatility for innovative tasks.

This repository provides a generic implementation of various compute-efficient models such as EfficientNet, MixNet, and MobileNetV3, rooted in MobileNet V1/V2 architecture blocks. Note: The repository is no longer maintained; consider the 'timm' library for more up-to-date features. Key updates have brought new MobileNet-V2 models with advanced parameters, improved EfficientNet variants with NoisyStudent and AdvProp optimizations, and refined ONNX/Caffe2 export capabilities, enhancing AI model validation and deployment with pretrained weights.

The project offers broad updates across multiple model types, providing fixes and support for modern architectural weights. Enhancements include the introduction of models like SigLIP SO400M ViT and RDNet, along with optimizations in MobileNet versions. Advanced configurations and improved hardware compatibility, including Intel Arc and Ascend NPUs, deliver notable performance improvements, making it a key resource for the deep learning community while aligning with current research trends.