Displaying 1 to 7 from 7 results

TNN - TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile态desktop and server

  •    C++

TNN: A high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN reasoning framework. Chinese OCR demo is the TNN implementation of chineseocr_lite project. It is lightweight and supports tilted, rotated and vertical text recognition.

OpenVINO-model-server - Inference model server implementation with Intel performance optimizations and TensorFlow Serving API

  •    Python

Inference model server implementation with gRPC interface, compatible with TensorFlow serving API and OpenVINO™ as the execution backend. “OpenVINO™ model server” is a flexible, high-performance inference serving component for artificial intelligence models. The software makes it easy to deploy new algorithms and AI experiments, while keeping the same server architecture and APIs like in TensorFlow Serving.

object-flaw-detector-cpp

  •    C++

Object flaw detector application detects anomalies such as color, crack and the orientation of an object moving on a conveyor belt. Anomalies are marked as defective and saved in the color, crack, orientation folders respectively. Also objects with no defects are saved in no_defect folder. These anomalies data are sent to InfluxDB* database and visualized on Grafana*. This application also measures length and width of the object in millimeters. This application accepts input from a video camera or a video file for processing.




BMW-IntelOpenVINO-Detection-Inference-API - This is a repository for a No-Code object detection inference API using the OpenVINO

  •    Python

This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems. Models in Intermediate Representation(IR) format, converted using the Intel® OpenVINO™ toolkit v2021.1, can be deployed in this API. Currently, OpenVINO supports conversion for Models trained in several Machine Learning frameworks including Caffe, Tensorflow etc. Please refer to the OpenVINO documentation for further details on converting your Model.

Keras_to_TF_NCS2 - Keras to Tensorflow test for Neural Compute Stick 2

  •    Python

For that you want to have OpenVino installed and python 3.5 at least. For the python requirements, see the "Requirements" section. If you have a Keras .h5 file, use keras_to_tf.py to create a Tensorflow .pb file.