Packages
Converts text to normalized form and back (inverse normalization) for speech recognition and synthesis pipelines, using weighted finite-state transducers and language models.
Reimplemented data augmentation transforms for deep learning, optimized for CPU performance and designed to work with torch and numpy on individual samples rather than batches.
However, verify that the currently implemented transforms cover your use case, since the package explicitly covers only a subset of the original batchgenerators.
Norfair adds real-time multi-object tracking to any detector by matching detected coordinates across frames using configurable distance functions and Kalman filtering.
Install it if you need real-time multi-object tracking in video and already have a detector.
Fits the smallest-area quadrilateral that contains all points from a noisy polygon or point cloud, useful for cleaning up irregular detections before perspective correction or pattern matching.
Install it if your workflow involves cleaning up shape detections; skip it if you need active support or ongoing feature development.
DocArray provides a Python data structure for representing, transmitting, storing, and retrieving multimodal data, with built-in support for tensors from NumPy, PyTorch, TensorFlow, and JAX.
Jina is a framework for building and deploying AI services that communicate via gRPC, HTTP, and WebSockets, with built-in support for scaling, containerization, and cloud deployment.
Not recommended if you prefer minimal dependencies or need cutting-edge feature velocity.
Automated machine learning for image classification and object detection, handling model selection, hyperparameter tuning, and training with minimal code.
However, the latest release is from 2023-01-11, so verify ongoing support and compatibility with your target environment before committing to production use.
Automates machine learning for text data, training and deploying high-accuracy models with minimal code through AutoGluon's text prediction interface.
Detects and segments QR codes in images using a YOLOv8-based model, returning bounding boxes, confidence scores, and precise polygon coordinates even in difficult positions.
QReader detects and decodes QR codes from images using a YOLOv8 model combined with Pyzbar, handling difficult and damaged QR codes through image preprocessing.
Install only if you can satisfy the system-level pyzbar dependencies and accept that the last release was in February 2025.
Megatron-FSDP implements Fully Sharded Data Parallelism (FSDP) in native PyTorch to distribute training and inference of large models across multiple GPUs with configurable memory-communication trade-offs.
Provides a collection of pretrained computer vision models for PyTorch, including image classification, segmentation, detection, and pose estimation architectures.
However, verify the license terms in the repository first, and confirm that model loading and normalization behavior match your requirements—the lack of recent…