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Tencent / TNN

Star 3.5k

TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.

ocr deep-learning tensorflow inference pytorch tengine face-detection tensorrt mnn coreml ncnn openvino hairsegmentaion
  • Updated Jun 6, 2022
  • C++

666DZY666 / micronet

Star 1.8k

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

pytorch pruning convolutional-networks quantization xnor-net tensorrt model-compression bnn neuromorphic-computing group-convolution onnx network-in-network tensorrt-int8-python dorefa twn network-slimming integer-arithmetic-only quantization-aware-training post-training-quantization batch-normalization-fuse
  • Updated Oct 6, 2021
  • Python

pytorch / TensorRT

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nebuly-ai / nebullvm

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Open

Upgrade OpenVino API to v2.0

morgoth95
morgoth95 commented Apr 24, 2022

The latest release of openvino has changed the inference engine API, providing a new API that takes advantage of the full potential of Intel's latest version of IR (IR 11). More information can be found at this link. We should adapt nebullvm's OpenVinoInferenceLearner to their latest API.

Usef

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enhancement good first issue

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