FER2013_VGG19,表情识别三个模型
表情识别模型 https://zhangzhe.blog.csdn.net/article/details/90210720
表情识别模型 https://zhangzhe.blog.csdn.net/article/details/90210720
Dataset from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data Image Properties: 48 x 48 pixels (2304 bytes) labels: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral The training set consists of 28,709 examples. The public test set consists of 3,589 examples. The private test set consists of another 3,589 examples. 原链接网速过慢,特上传一份仅供学习
详见https://zhangzhe.blog.csdn.net/article/details/115127428 包含labels.txt、yolov5s.wts、libmyplugins.so、yolov5s.engine
1.安装cmake sudo apt install cmake tar -zxv -f cmake-3.13.3.tar.gz cd cmake-3.13.3 ./bootstrap make make install 2.安装openssl和编译依赖 sudo apt install openssl sudo apt install libssl-dev
https://github.com/NVIDIA-AI-IOT/trt_pose This project features multi-instance pose estimation accelerated by NVIDIA TensorRT. It is ideal for applications where low latency is necessary. It includes Training scripts to train on any keypoint task data in MSCOCO format A collection of models that may be easily optimized with TensorRT using torch2trt This project can be used easily for the task of human pose estimation, or extended for something new. contains convert pytorch model into tensorrt model
https://github.com/NVIDIA-AI-IOT/trt_pose This project features multi-instance pose estimation accelerated by NVIDIA TensorRT. It is ideal for applications where low latency is necessary. It includes Training scripts to train on any keypoint task data in MSCOCO format A collection of models that may be easily optimized with TensorRT using torch2trt This project can be used easily for the task of human pose estimation, or extended for something new. Below are models pre-trained on the MSCOCO dataset. The throughput in FPS is shown for each platform
具体参考:https://blog.csdn.net/qq_42393859/article/details/85251356
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