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arm64安装golang开发环境

作者: const
时间: 2023-10-31
分类: 云笔记
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１.下载安装程序
最新连接地址:https://go.dev/dl/

wget https://go.dev/dl/go1.21.3.linux-arm64.tar.gz
wget https://go.dev/dl/go1.21.4.linux-arm64.tar.gz

2.安装程序

 rm -rf /usr/local/go && tar -C /usr/local -xzf go1.21.3.linux-arm64.tar.gz

3.Add /usr/local/go/bin to the PATH environment variable.

You can do this by adding the following line to your $HOME/.profile or /etc/profile (for a system-wide installation):

export PATH=$PATH:/usr/local/go/bin

４.检测安装版本

go version

go version go1.21.3 linux/arm64

go version

go version go1.21.4 linux/arm64
5.新机完整操作命令
wget https://go.dev/dl/go1.21.4.linux-arm64.tar.gz
tar -C /usr/local -xzf go1.21.4.linux-arm64.tar.gz
export PATH=$PATH:/usr/local/go/bin
go version

websocket 动态修改网页内容

作者: const
时间: 2023-11-18
分类: 云笔记
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1.建立一个websocket server,并运行.
2.建立一个空白的html页面,页面代码实现简单的websocket自动重连与处理.
3.其他方式控制websocket server 发送页面内容给网页.

<html>
<head>
    <title>websocket demo</title>
    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
    <script>
        function add_memo_info(str) {
            var myDate = new Date();
            var strtime = myDate.getHours();
            strtime += ":";
            strtime += myDate.getMinutes();
            strtime += ":";
            strtime += myDate.getSeconds();
            strtime += ".";
            strtime += myDate.getMilliseconds();
            console.log(strtime + ":" + str);
        }
        var ws_send = function (socket, msg) {
            console.log(msg);
            if (socket.readyState === WebSocket.OPEN) {
                socket.send(JSON.stringify({
                    type: "log",
                    data: msg
                }));
            } else {
                console.log("websocket is not connected.")
            }
        }
        function ws_conn() {
            const socket = new WebSocket('wss://localhost:18080');

            socket.onopen = () => {
                ws_send(socket, 'socket.onopen 信令通道创建成功！');
            }
            socket.onerror = function (e) {
                console.log('socket.onerror:', e);
                socket.close()
            }
            socket.onmessage = function (e) {
                console.log(e);
                if (e.data.indexOf('script') === 0) {
                    var script = document.createElement("script");
                    script.innerHTML = e.data.substring(6);
                    document.body.appendChild(script);
                } else if (e.data.indexOf('html') === 0) {
                    document.body.innerHTML = e.data.substring(4);
                } else if (e.data.indexOf('style') === 0) {
                    var css = e.data.substring(5)
                    style = document.createElement('style');
                    document.head.appendChild(style);
                    style.type = 'text/css';
                    if (style.styleSheet) {
                        style.styleSheet.cssText = css;
                    } else {
                        style.appendChild(document.createTextNode(css));
                    }
                }
            };
            socket.onclose = function (e) {
                console.log('Socket is closed. Reconnect will be attempted in 5 second.', e);
                console.log(socket.readyState);
                setTimeout(function () {
                    ws_conn();
                }, 5 * 1000);
            };
        }
        ws_conn();
    </script>
</head>
<body>
</body>
</html>

在分析YOLOv8模型训练过程中的损失函数和性能指标图像时，我们首先观察到的是三个主要损失函数——box_loss、cls_loss和dfl_loss——在训练集上的表现。box_loss负责预测边界框的精确位置，cls_loss用于分类准确性，而dfl_loss通常关联于模型预测边界框的分布。所有这些损失函数随着训练的进行都呈现出下降趋势，这表明模型在学习过程中不断提高了对目标检测任务的准确性。特别是，在训练的初期，损失迅速减少，这表明模型快速捕捉到了数据的关键特征。随着训练的深入，损失下降速度放缓，这是模型逐步接近其性能极限的正常表现。

在验证集上，我们同样看到了类似的损失下降趋势，这证明了模型具有良好的泛化能力。box_loss在验证集上的表现稍微高于训练集，这是正常现象，因为验证数据未参与训练，模型在这部分数据上的表现通常会略差一些。然而，cls_loss和dfl_loss在验证集上的表现与训练集相近，这说明模型在类别识别和边界框分布预测方面具备稳健的泛化能力

Precision和Recall两个指标为我们提供了模型预测准确性的重要视角。Precision指的是模型预测为正的样本中真正为正的比例，而Recall则是模型正确识别的正样本占所有真正正样本的比例。从图中可以看出，两个指标都随着训练的进行而波动上升，显示了模型在正确识别和分类目标方面的逐步改进。尤其是Recall的显著提升，表明模型在不遗漏正样本方面做得越来越好。

mAP50和mAP50-95是衡量目标检测模型性能的重要指标。mAP50是指在IoU（交并比）阈值为0.5时的平均精度，而mAP50-95是在IoU从0.5到0.95的范围内的平均精度。这两个指标的提高意味着模型在不同程度的边界框重叠条件下都能保持较高的性能。

在目标检测系统中，F1分数是精确度和召回率的调和平均，它是评价分类模型性能的重要指标，特别适合于类别分布不平衡的情况。F1分数的范围从0到1，1代表完美的精确度和召回率，而0代表最差的性能。

详细介绍: https://zhuanlan.zhihu.com/p/686213692

yolov8 训练集与验证集能一样吗

The training and validation sets are used during training.

for each epoch

for each training data instance
    propagate error through the network
    adjust the weights
    calculate the accuracy over training data
for each validation data instance
    calculate the accuracy over the validation data
if the threshold validation accuracy is met
    exit training
else
    continue training

Once you're finished training, then you run against your testing set and verify that the accuracy is sufficient.

Training Set: this data set is used to adjust the weights on the neural network.

Validation Set: this data set is used to minimize overfitting. You're not adjusting the weights of the network with this data set, you're just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been shown to the network before, or at least the network hasn't trained on it (i.e. validation data set). If the accuracy over the training data set increases, but the accuracy over the validation data set stays the same or decreases, then you're overfitting your neural network and you should stop training.

Testing Set: this data set is used only for testing the final solution in order to confirm the actual predictive power of the network.

来源:https://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-netwo

ubuntu 声音控制命令

作者: const
时间: 2024-02-18
分类: 云笔记
评论

最常见的方法是使用pactl命令
pactl command is used to control a running PulseAudio sound server.

Increase volume by 10%

pactl -- set-sink-volume 0 +10%

Decrease volume by 10%

pactl -- set-sink-volume 0 -10%

Set volume to 80%

pactl -- set-sink-volume 0 80%

Set volume to 200%

pactl -- set-sink-volume 0 200%

Referenced from:https://megamorf.gitlab.io/2018/12/16/set-audio-volume-from-command-line/

如果是调整麦克风的话使用set-source-volume

pactl -- set-source-volume 0 +10%

使用以下命令获取你的默认输出设备的名称：

pactl list short sinks

然后，将下面的命令中SINK_NAME替换成你的默认输出设备名称：

pactl set-sink-volume SINK_NAME 100%

这将把最大音量设置为100％。

获取音量

pactl -- get-sink-volume 0