七月在線 課程：https://www.julyedu.com/course/getDetail/262

什么是關鍵點？

關鍵點定義：關鍵點也稱為興趣點，它是2D圖像、3D點云或曲面模型上，可以通過定義檢測標準來獲取的具有穩定性、區別性的點集。關鍵點檢測涉及同時檢測人和定位他們的關鍵點。關鍵點與興趣點相同。它們是空間位置或圖像中的點，它們定義了圖像中有趣或突出的內容。它們對圖像旋轉、收縮、平移、失真等是不變的。

關鍵點的意義？

加快后續識別、追蹤等數據的處理速度。

環境配置

nvidia GPU 配置：

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

code ： MNIST

MNIST實戰！

import torch from torchvision import datasets, transforms import matplotlib.pyplot as plt import os import torchvision import numpy as np from torch.autograd import Variable import random %matplotlib inlinetransform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])data_train = datasets.MNIST(root = "./data/",transform=transform,train = True,download = True)data_test = datasets.MNIST(root="./data/",transform = transform,train = False)data_loader_train = torch.utils.data.DataLoader(dataset=data_train,batch_size = 64,shuffle = True,num_workers=2)data_loader_test = torch.utils.data.DataLoader(dataset=data_test,batch_size = 64,shuffle = True,num_workers=2)images, labels = next(iter(data_loader_train)) img = torchvision.utils.make_grid(images)img = img.numpy().transpose(1,2,0) std = [0.5,0.5,0.5] mean = [0.5,0.5,0.5] img = img*std+mean print([labels[i] for i in range(64)]) plt.imshow(img)class Model(torch.nn.Module):def __init__(self):super(Model, self).__init__()self.conv1 = torch.nn.Sequential(torch.nn.Conv2d(1,64,kernel_size=3,stride=1,padding=1),torch.nn.ReLU(),torch.nn.Conv2d(64,128,kernel_size=3,stride=1,padding=1),torch.nn.ReLU(),torch.nn.MaxPool2d(stride=2,kernel_size=2))self.dense = torch.nn.Sequential(torch.nn.Linear(14*14*128,1024),torch.nn.ReLU(),torch.nn.Dropout(p=0.5),torch.nn.Linear(1024, 10))def forward(self, x):x = self.conv1(x)x = x.view(-1, 14*14*128)x = self.dense(x)return xcost = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters()) n_epochs = 5for epoch in range(n_epochs):running_loss = 0.0running_correct = 0print("Epoch {}/{}".format(epoch, n_epochs))print("-"*10)for data in data_loader_train:X_train, y_train = dataX_train, y_train = Variable(X_train), Variable(y_train)outputs = model(X_train)_,pred = torch.max(outputs.data, 1)optimizer.zero_grad()loss = cost(outputs, y_train)loss.backward()optimizer.step() #進行單次優化running_loss += loss.datarunning_correct += torch.sum(pred == y_train.data)testing_correct = 0for data in data_loader_test:X_test, y_test = dataX_test, y_test = Variable(X_test), Variable(y_test)outputs = model(X_test)_, pred = torch.max(outputs.data, 1)testing_correct += torch.sum(pred == y_test.data)print("Loss is:{:.4f}, Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}".format(running_loss/len(data_train),100*running_correct/len(data_train),100*testing_correct/len(data_test))) torch.save(model.state_dict(), "model_parameter.pkl")

reference resources

• https://paperswithcode.com/sota/keypoint-detection-on-coco-test-dev

總結

以上是生活随笔為你收集整理的七月在线 《关键点检测概览与环境配置》的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。