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RetinaFace主要有四个特点:结构特点有FPN、单阶段、上下文建模、多任务学习 a.FPN 类似于retinanet,作者采用 FPN 中的 Feature Pyramid 结构,并以 ResNet-152 作为 Backbone,其中, C2/C3/C4/C5 为 ResNet 中各个 Residual Block 所生成的 Feature Map,而 C6 由 C5 经过 3*3 的卷积层生成 ...

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May 16, 2018 · State of the art (2019) face detection with RetinaFace and MXNet. 17. July 2019. Protection from AI. 5. September 2020. Post navigation ← Previous.

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State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation. Keras-MXNet. Keras-MXNet provides a backend support for the widely used high level API Keras. MXBoard. MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard.

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RetinaFace-Anti-Cov is a customized one stage face detector to help people protect themselves from CovID-19. More details provided in the paper and repository Specification

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Recent Posts. Dont talk to me that way: Trump lashes out at White House reporter over election question; Mike Tyson says psychedelic drug told me to come back and start getting in shape

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RetinaFace 由 InsightFace 和帝国理工大学联合提出(InsightFace 为目前针对 2D 与 3D 人脸分析(含检测、识别、对齐、属性识别等)最知名和开发者最活跃的开源库),是目前开源的人脸检测算法中效果最好的算法(仅比 AlnnoFace 低一点点)。

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The following are 30 code examples for showing how to use mxnet.ndarray.array().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Then my Raspberry arrived and i saw that this isn't going to run well, so i switched to Retinaface-Mobilenet-0.25 (Mxnet) which was pre-trained better than anything i trained myself for accuracy, but still not good enough framerate. I'll read into the onnx runtime - thanks for the tip.

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C C++ CMake CNN Eigen GAN Linux Matlab NB-IOT OJ PCB Qt c gan git k210 keras linux mindspore mxnet pfld python pytorch retinaface stm32 tensorflow vscode wordcloud yolo 二叉树 作业 元学习 半监督学习 博客 图像处理 堆栈 声音信号处理 小工具 嵌入式 总结 排序 数据结构 机器学习 树 树莓派 概率论 ...

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C C++ CMake CNN Eigen GAN Linux Matlab NB-IOT OJ PCB Qt c gan git k210 keras linux mindspore mxnet pfld python pytorch retinaface stm32 tensorflow vscode wordcloud yolo 二叉树 作业 元学习 半监督学习 博客 图像处理 堆栈 声音信号处理 小工具 嵌入式 总结 排序 数据结构 机器学习 树 树莓派 概率论 ...

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State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation. Keras-MXNet. Keras-MXNet provides a backend support for the widely used high level API Keras. MXBoard. MXBoard provides a set of APIs for logging MXNet data for visualization in TensorBoard.
Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF ...
在WILDER FACE hard子集上,RetinaFace的性能比目前the state of the art的two-stage方法(ISRN)的AP高出1.1% (AP等于91.4%)。 在IJB-C数据集上,RetinaFace有助于提高ArcFace的验证精度(FAR=1e-6时TAR等于89:59%)。这表明更好的人脸定位可以显著提高人脸识别。
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity. For feature requests on the PyPI package, suggestions, and issue reports, create an issue by clicking here.
detectors in Table3. Compared to the RetinaFace with DCNv2, our ProgressFace-Light costs similar inference time (6.92 ms vs. 6.80 ms) but achieves better per-formance on WIDER FACE validation set (e.g., 87.9% vs. 79.5% for the hard set). 5 Di erences between our anchor-free module and SFace

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Face Analysis Project on MXNet. Citation. If you find InsightFace useful in your research, please consider to cite the following related papers:. @inproceedings{deng2019retinaface, title={RetinaFace: Single-stage Dense Face Localisation in the Wild}, author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos}, booktitle={arxiv}, year={2019 ...
Oct 11, 2019 · 2. On the WIDER FACE hard subset, RetinaFace outperforms the AP of the state of the art two-stage method. 3. On the IJB-C dataset, RetinaFace helps to improve ArcFace’s verification accuracy. 4. By employing light-weight backbone networks, RetinaFace can run real-time on a single CPU core for a VGA-resolution image. 5. Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX). Sockeye It implements state-of-the-art encoder-decoder architectures.