#image classification

The Differentiable Architecture Search method leverages continuous relaxation and gradient descent for efficient design of convolutional and recurrent architectures applicable in image classification and language modeling. Compatible with a single GPU setup, it targets datasets like CIFAR-10, ImageNet, PTB, and WikiText-2. Pretrained models facilitate swift evaluations, while 2nd-order approximations aid in searching for optimal architectures based on validation outcomes. Comprehensive training with full-sized models verifies their performance. Visualization tools further enhance understanding of architectural design, all within Python and PyTorch environments, providing a notable advancement for machine learning architecture optimization.

MambaVision is a hybrid vision backbone merging self-attention and mixer blocks, achieving leading performance in accuracy and throughput. Utilizing a symmetric path without SSM, this PyTorch model enhances global context processing. Available on Hugging Face and GitHub, MambaVision pre-trained models process images of any resolution, adhering to CC-BY-NC-SA-4.0 licensing. Suitable for tasks like classification, detection, and segmentation, it offers multi-scale features over 4 stages. Integration is seamless via pip or Hugging Face.

Designed to democratize access to machine learning, Turi Create facilitates the easy development of custom models for individuals without technical expertise. This intuitive tool seamlessly integrates complex tasks such as recommendations, object detection, and image classification into applications, supporting various data types including text, images, audio, and video. With scalable data processing on a single machine, it allows exporting models to Core ML for use within Apple's ecosystem, covering iOS, macOS, watchOS, and tvOS. Turi Create offers flexibility and ease of use, with built-in visualizations for data exploration, and supports a wide range of tasks from regression to style transfer, making it ideal for developers across various domains.

Discover how the OpenAI Vision API powers imaginative experiments in visual AI for novices and veterans alike. Explore limitations and integrations, such as combining GPT-4V with GroundingDINO, to surpass current capabilities.

Explore up-to-date tools for object detection and image processing with YOLO11, a cutting-edge model known for its rapid speed, accuracy, and versatility. This model supports a variety of tasks such as object detection, tracking, instance segmentation, image classification, and pose estimation. Building on the success of prior YOLO models, it delivers enhanced performance and intuitive features. Access detailed documentation, community forums, and installation options for seamless project integration. Engage with the developer community for further insights and practical applications of YOLO11.

ARES 2.0 is a Python library designed for evaluating adversarial robustness in image classification and object detection models. Utilizing PyTorch, it supports diverse attacks, offers robust training strategies, and includes trained checkpoints. The library facilitates distributed training and testing, making it suitable for adversarial machine learning research. Comprehensive setup instructions and detailed documentation support ease of use and implementation.

Metalhead.jl offers a diverse collection of machine learning vision models designed for seamless integration with Flux.jl, featuring frameworks such as AlexNet, ResNet, and Vision Transformer. It implements pure Flux layers, ensuring adherence to best practices in module development, and provides resources for building intricate models. This package is suitable for developers intending to apply state-of-the-art machine learning methodologies for image classification, offering detailed guides and comprehensive API documentation to aid in incorporating pre-trained models or contributing novel ones.