thtrieu/darkflow
Darkflow
Darkflow es un framework de detección de objetos y pipeline de visión artificial que proporciona una interfaz programática para realizar análisis de imágenes y detección de objetos en tiempo real. Funciona como una herramienta para cargar pesos, ajustar modelos y ejecutar inferencia tanto en imágenes estáticas como en feeds de video.
El proyecto sirve como un convertidor que traduce configuraciones y pesos de Darknet a grafos de TensorFlow para permitir el reentrenamiento y el despliegue. Incluye un exportador de modelos que guarda los grafos entrenados en archivos protobuf portátiles para su uso en dispositivos móviles y nativos.
El sistema cubre capacidades para entrenar y ajustar modelos de detección en datasets personalizados, con puntos de control de progreso para la recuperación del entrenamiento. También proporciona herramientas para la traducción de mapeo de pesos y el procesamiento de datos de imagen crudos a través de operaciones de tensores para producir cajas delimitadoras y puntuaciones de confianza.
Features
- Object Detection - Identifies and locates objects within static images, returning bounding boxes, labels, and confidence scores.
- Computer Vision Pipelines - Provides an end-to-end computer vision pipeline for loading weights, fine-tuning models, and performing inference.
- Custom Model Training - Allows the import of existing weights to perform additional training on custom datasets for specific tasks.
- Detection Model Training - Trains and fine-tunes object detection models on custom datasets to classify specific objects.
- Model Inference APIs - Provides a Python-based interface to pass image arrays into detection models and receive structured results as dictionaries.
- Computer Vision Model Integration - Integrates pre-trained weights for object detection tasks using a programmatic Python interface.
- Object Detection Frameworks - Provides a comprehensive framework for real-time image analysis and object identification using deep learning.
- Cross-Framework Converters - Translates Darknet configurations and weights into TensorFlow graphs for retraining and deployment.
- Framework Converters - Translates Darknet models and weights into TensorFlow graphs to enable retraining and deployment.
- Real-Time Video Analysis - Analyzes live camera feeds or video files in real-time to identify objects and save processed output.
- Model Graph Conversion - Translates Darknet configuration files into TensorFlow computational graphs for execution and retraining.
- Portable Model Exporters - Converts trained graphs into portable protobuf files for mobile and native device deployment.
- Runtime Weight Loading - Implements runtime weight loading to align external binary weights with the target runtime's tensor dimensions.
- Training Checkpointers - Provides mechanisms for saving and resuming model training states to ensure session continuity.
- Weight Parameter Mapping - Provides utilities to map Darknet binary weight names and shapes to TensorFlow tensor variables.
- Model Export Formats - Saves trained graphs and weights into portable binary formats like Protobuf for deployment on mobile devices.
- Detection Model Exporters - Serializes trained detection models into portable Protobuf formats for deployment on mobile devices.
- Tensor Processing Pipelines - Implements a tensor processing pipeline that handles image batches as multi-dimensional arrays for GPU-accelerated inference.
- Mobile Model Deployment - Exports trained models into portable protobuf formats for execution on mobile devices.
- Computer Vision Libraries - TensorFlow port of the Darknet object detection framework.
- Computer Vision Models - Real-time object detection optimized for mobile device deployment.
- Object Detection - TensorFlow implementation of real-time object detection.
