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wordpress文章全部刪除優(yōu)化seo深圳

news 2025/7/4 1:20:35
wordpress文章全部刪除,優(yōu)化seo深圳,哪些網(wǎng)站用黑體做的,調(diào)查問卷網(wǎng)站建設(shè)方案**實(shí)驗(yàn) 深度學(xué)習(xí)與應(yīng)用：行人跟蹤 ** ------ **1、 實(shí)驗(yàn)?zāi)康?* ------ - 了解行人跟蹤模型基礎(chǔ)處理流程 - 熟悉行人跟蹤模型的基本原理 - 掌握 行人跟蹤模型的參數(shù)微調(diào)訓(xùn)練以及推理的能力 - 掌握行人跟蹤模型對(duì)實(shí)際問題的應(yīng)用能力，了解如何在特定的場(chǎng)景和…

**實(shí)驗(yàn) ?深度學(xué)習(xí)與應(yīng)用：行人跟蹤 **
------
**1、 實(shí)驗(yàn)?zāi)康?*
------
- 了解行人跟蹤模型基礎(chǔ)處理流程
- 熟悉行人跟蹤模型的基本原理
- 掌握 行人跟蹤模型的參數(shù)微調(diào)訓(xùn)練以及推理的能力
- 掌握行人跟蹤模型對(duì)實(shí)際問題的應(yīng)用能力，了解如何在特定的場(chǎng)景和任務(wù)中應(yīng)用該模型


**2、實(shí)驗(yàn)環(huán)境**
------
**[鏡像詳情]**
虛擬機(jī)數(shù)量：1個(gè)（需GPU ?>=4GB）
虛擬機(jī)信息：

1. 操作系統(tǒng)：Ubuntu20.04

2. 代碼位置：/home/zkpk/experiment/yolo_tracking_main

3. MOT17數(shù)據(jù)集存儲(chǔ)位置：examples/val_utils/data/MOT17
? ?(數(shù)據(jù)集下載地址：Https://motchallenge.net)

4. 已安裝軟件：python版本：python 3.9，顯卡驅(qū)動(dòng)，cuda版本：cuda11.3 cudnn 版本：8.4.1,torch==1.12.1+cu113,torchvision= 0.13.1+cu113
5. 根據(jù)requirements.txt,合理配置python環(huán)境

**3、實(shí)驗(yàn)內(nèi)容**
------
- 準(zhǔn)備多目標(biāo)跟蹤數(shù)據(jù)集MOT17 ，下載地址位于（Https://motchallenge.net），放置于工程路徑為：（examples/val_utils/data/MOT17） ?
- 根據(jù)不用的行人跟蹤算法實(shí)現(xiàn)行人跟蹤實(shí)驗(yàn)
- 根據(jù)實(shí)驗(yàn)效果微調(diào)行人跟蹤算法模型參數(shù)
- 實(shí)現(xiàn)離線視頻的行人跟蹤


**4、實(shí)驗(yàn)關(guān)鍵點(diǎn)**
------
- ?下載數(shù)據(jù)集放置于指定的文件夾下
- ?配置好算法所需的python虛擬環(huán)境
- ?掌握行人跟蹤所需的算法基礎(chǔ)
- ?具備一定的代碼能力，解決實(shí)際問題

? ?
**5、實(shí)驗(yàn)效果圖**
------
行人跟蹤效果截圖：
![](media/798ashdh.png)
? <center>圖 1</center> ?

行人跟蹤視頻效果：

目標(biāo)跟蹤

**6、實(shí)驗(yàn)步驟**
------
- 6.1 準(zhǔn)備數(shù)據(jù)集，下載多目標(biāo)跟蹤數(shù)據(jù)集MOT17 ，下載地址位于（Https://motchallenge.net），將數(shù)據(jù)集放置于（examples/val_utils/data/MOT17）路徑，如下圖所示：
??
? <center>圖 1</center> ?
- 6.2 實(shí)現(xiàn)行人跟蹤方法對(duì)視頻的實(shí)時(shí)檢測(cè)，運(yùn)行一下命令進(jìn)入yolo_tracking_main\examples： ?
??
? ```shell
? cd ? /home/zkpk/experiment/yolo_tracking_main/examples
? ```
運(yùn)行python的track.py腳本，命令如下：
```shell
python --yolo-model weights/yolov8n --tracking-method ?deepocsort ?----reid-model ?weights/lmbn_n_cuhk03_d.pt ?--source ?testvideo.mp4 ? --conf ?0.3 ?--iou ?0.5 ?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?botsort ?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?strongsort
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?ocsort ?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?bytetrack

```
分別對(duì)應(yīng)5種不同的目標(biāo)跟蹤模型，實(shí)現(xiàn)對(duì)行人目標(biāo)的跟蹤

運(yùn)行日志如下：
```
Successfully loaded imagenet pretrained weights from "weights\osnet_x1_0_imagenet.pth"
video 1/1 (1/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 63.4ms
video 1/1 (2/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (3/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (4/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (5/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (6/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (7/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (8/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (9/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (10/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (11/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (12/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 9.0ms
video 1/1 (13/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (14/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (15/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (16/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (17/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 9.0ms
video 1/1 (18/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (19/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (20/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (21/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (22/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (23/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (24/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 15.0ms
video 1/1 (25/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (26/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 10.0ms
video 1/1 (27/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 11.0ms
video 1/1 (28/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 13.0ms
video 1/1 (29/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 4 persons, 13.0ms
video 1/1 (30/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 4 persons, 13.0ms
video 1/1 (31/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms
video 1/1 (32/2385) E:\PycharmProjects\yolo_tracking_main\examples\testvideo.mp4: 480x640 3 persons, 14.0ms

```
6.3 根據(jù)上一步驟6.3 行人跟蹤的效果，假如不理想可以使用MOT17數(shù)據(jù)集微調(diào)模型參數(shù)(在配置好數(shù)據(jù)集的情況才可以微調(diào))，運(yùn)行一下命令：
``` shell
python ?--yolo-model weights/yolov8n.pt --tracking-method ?deepocsort --benchmark ?MOT17 ?--conf ?0.45

```

微調(diào)參數(shù)過程日志如下：

```
2023-11-17 17:34:48.482 | INFO ? ? | val:eval:204 - Staring evaluation process on E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1
2023-11-17 17:34:48.560 | INFO ? ? | val:eval:204 - Staring evaluation process on E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1


2023-11-17 17:35:00.221 | SUCCESS ?| boxmot.appearance.reid_model_factory:load_pretrained_weights:207 - Successfully loaded pretrained weights from "E:\PycharmProjects\yolo_tracking_main\examples\weights\osnet_x0_25_msmt17.pt"
2023-11-17 17:35:00.221 | WARNING ?| boxmot.appearance.reid_model_factory:load_pretrained_weights:211 - The following layers are discarded due to unmatched keys or layer size: ('classifier.weight', 'classifier.bias')
2023-11-17 17:35:00.228 | SUCCESS ?| boxmot.appearance.reid_model_factory:load_pretrained_weights:207 - Successfully loaded pretrained weights from "E:\PycharmProjects\yolo_tracking_main\examples\weights\osnet_x0_25_msmt17.pt"
2023-11-17 17:35:00.228 | WARNING ?| boxmot.appearance.reid_model_factory:load_pretrained_weights:211 - The following layers are discarded due to unmatched keys or layer size: ('classifier.weight', 'classifier.bias')
image 1/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000001.jpg: 736x1280 11 persons, 610.4ms
image 1/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000001.jpg: 736x1280 25 persons, 652.3ms
image 2/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000002.jpg: 736x1280 9 persons, 442.2ms
image 2/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000002.jpg: 736x1280 22 persons, 454.5ms
image 3/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000003.jpg: 736x1280 9 persons, 370.0ms
image 3/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000003.jpg: 736x1280 24 persons, 450.9ms
image 4/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000004.jpg: 736x1280 9 persons, 460.8ms
image 4/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000004.jpg: 736x1280 23 persons, 385.0ms
image 5/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000005.jpg: 736x1280 10 persons, 460.4ms
image 5/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000005.jpg: 736x1280 22 persons, 399.6ms
image 6/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000006.jpg: 736x1280 10 persons, 443.0ms
image 7/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000007.jpg: 736x1280 10 persons, 460.8ms
image 6/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000006.jpg: 736x1280 22 persons, 429.7ms
image 8/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000008.jpg: 736x1280 10 persons, 434.5ms
image 7/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000007.jpg: 736x1280 24 persons, 448.3ms
image 9/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000009.jpg: 736x1280 10 persons, 386.9ms
image 8/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000008.jpg: 736x1280 23 persons, 476.7ms
image 10/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000010.jpg: 736x1280 11 persons, 869.1ms
image 9/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000009.jpg: 736x1280 23 persons, 453.9ms
image 11/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000011.jpg: 736x1280 11 persons, 460.8ms
image 10/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000010.jpg: 736x1280 23 persons, 428.2ms
image 12/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000012.jpg: 736x1280 9 persons, 439.9ms
image 11/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000011.jpg: 736x1280 19 persons, 470.3ms
image 13/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000013.jpg: 736x1280 10 persons, 440.8ms
image 12/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000012.jpg: 736x1280 19 persons, 460.8ms
image 14/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000014.jpg: 736x1280 9 persons, 434.2ms
image 13/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000013.jpg: 736x1280 20 persons, 439.0ms
image 15/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000015.jpg: 736x1280 8 persons, 384.9ms
image 14/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000014.jpg: 736x1280 20 persons, 440.8ms
image 16/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000016.jpg: 736x1280 8 persons, 462.8ms
image 15/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000015.jpg: 736x1280 20 persons, 451.8ms
image 17/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000017.jpg: 736x1280 8 persons, 470.7ms
image 16/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000016.jpg: 736x1280 22 persons, 486.0ms
image 18/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000018.jpg: 736x1280 7 persons, 410.9ms
image 17/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000017.jpg: 736x1280 23 persons, 425.9ms
image 19/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000019.jpg: 736x1280 7 persons, 380.0ms
image 20/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000020.jpg: 736x1280 8 persons, 436.8ms
image 18/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000018.jpg: 736x1280 23 persons, 447.8ms
image 21/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000021.jpg: 736x1280 8 persons, 476.0ms
image 19/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000019.jpg: 736x1280 21 persons, 518.6ms
image 22/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000022.jpg: 736x1280 8 persons, 360.0ms
image 20/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000020.jpg: 736x1280 22 persons, 388.6ms
image 23/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000023.jpg: 736x1280 9 persons, 391.0ms
image 21/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000021.jpg: 736x1280 22 persons, 416.9ms
image 24/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000024.jpg: 736x1280 9 persons, 458.8ms
image 22/1050 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-04-FRCNN\img1\000022.jpg: 736x1280 22 persons, 404.7ms
image 25/600 E:\PycharmProjects\yolo_tracking_main\examples\val_utils\data\MOT17\train\MOT17-02-FRCNN\img1\000025.jpg: 736x1280 9 persons, 443.9ms
```

**7、思考題**
------
- ?考慮在行人跟蹤中，模型算法還有哪些改進(jìn)點(diǎn)
- ?思考怎么將跟蹤算法模型應(yīng)用到手部動(dòng)作跟蹤中
- ?思考如何調(diào)節(jié)模型參數(shù)和訓(xùn)練參數(shù)提升模型的效果指標(biāo)

**8、 實(shí)驗(yàn)報(bào)告**
------
請(qǐng)按照實(shí)驗(yàn)報(bào)告的格式要求撰寫實(shí)驗(yàn)報(bào)告。

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