balancap/SSD-Tensorflow
SSD Tensorflow
Este proyecto es un framework de detección de objetos de TensorFlow diseñado para entrenar y desplegar modelos Single Shot MultiBox Detector (SSD). Proporciona un toolkit de entrenamiento de redes neuronales para implementar la arquitectura SSD para lograr la localización de objetos en imágenes y videos en tiempo real.
El framework incluye un pipeline de datos dedicado para transformar datasets de detección de objetos en formatos de registro binario para aumentar la velocidad y el rendimiento del entrenamiento. También cuenta con utilidades para convertir pesos de modelos entre diferentes formatos de checkpoint para facilitar la reutilización de redes preentrenadas.
El sistema cubre una amplia gama de capacidades, incluyendo ajuste fino de modelos en datasets personalizados, entrenamiento de detección de objetos y evaluación de precisión a través de la medición de métricas de precisión y recall.
Features
- Object Detection Models - Implements the Single Shot MultiBox Detector (SSD) architecture for fast and accurate real-time object localization.
- Object Detection - Implements a system to identify and locate multiple objects within images using neural networks.
- Real-Time Object Detection - Provides high-speed inference for identifying and locating multiple objects in live video streams.
- Detection Model Training - Provides the capability to build detection networks using custom datasets and optimizer settings.
- Neural Network Training Toolkits - Provides a comprehensive software suite for the iterative development, training, and optimization of detection models.
- TensorFlow Model Development - Offers a complete framework for designing, building, and training SSD models within the TensorFlow ecosystem.
- Dataset Preprocessing Tools - Includes utilities for converting raw object detection data into binary record formats for optimized TensorFlow ingestion.
- Model Fine-Tuning - Supports adapting pre-trained detection models to specific tasks by updating weights on new datasets.
- Object Detection Evaluators - Provides tools to measure model performance by comparing predicted bounding boxes against ground truth for precision and recall.
- Detection Accuracy Evaluators - Calculates precision and recall metrics to quantify the localization and classification accuracy of detection models.
- Vision Model Fine-Tuning - Allows adapting pre-trained detection networks to new specific tasks by updating weights on custom datasets.
- TFRecord Data Processing - Ships a data conversion utility for serializing object detection datasets into TFRecord binary format.
- Binary Record Converters - Provides a dedicated pipeline for transforming object detection datasets into binary record formats for faster training.
