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All language subtitles for 003 How to run your first Deep Learning program in 3 minutes

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,090 --> 00:00:02,250 In this video, we will do object detection. 2 00:00:03,300 --> 00:00:05,880 First download the whole of seven states. 3 00:00:07,350 --> 00:00:11,070 We open your browser and download the all of 7.0 and X file. 4 00:00:18,390 --> 00:00:20,220 Wait until the download is finished. 5 00:00:30,760 --> 00:00:33,790 When you're finished, go to the downloads folder and open the file. 6 00:00:35,850 --> 00:00:41,940 Copy the words file and save it in the URL of seven python folder copy by pressing the control key. 7 00:00:45,140 --> 00:00:48,740 Placed in the order of seven python, followed by pressing the control fee. 8 00:00:52,180 --> 00:00:55,360 Then we open the command prompt to perform object detection. 9 00:00:55,870 --> 00:00:59,320 We will first detect objects on the road and for video. 10 00:01:00,130 --> 00:01:01,150 The command below. 11 00:01:01,900 --> 00:01:02,830 Tighten detection. 12 00:01:02,830 --> 00:01:03,580 Dot pie. 13 00:01:05,470 --> 00:01:05,850 That's. 14 00:01:05,860 --> 00:01:07,090 That's why it's your office. 15 00:01:07,090 --> 00:01:07,690 Seven. 16 00:01:10,250 --> 00:01:13,250 That's death Source Data Videos Roadmap for. 17 00:01:14,110 --> 00:01:14,980 The new enter. 18 00:01:15,840 --> 00:01:18,210 The detection results will then look like this. 19 00:01:25,450 --> 00:01:26,920 So exit press Q. 20 00:01:29,900 --> 00:01:33,530 If the Texan appears to be slow, you can use all of his seven tiny. 21 00:01:34,510 --> 00:01:35,890 Enter the command below. 22 00:01:36,580 --> 00:01:38,230 Heighten detection dog pipe. 23 00:01:40,920 --> 00:01:43,350 That's that's why it's YOLO vs seven tiny. 24 00:01:45,710 --> 00:01:48,740 That's death Source Data Video Roadmap for. 25 00:01:50,590 --> 00:01:51,420 Then hit enter. 26 00:01:53,010 --> 00:01:55,350 The detection results will then look like this. 27 00:01:56,900 --> 00:02:00,290 The detection appears to be faster, but the accuracy is reduced. 28 00:02:08,449 --> 00:02:10,680 Max detect objects in the image. 29 00:02:10,699 --> 00:02:13,370 The only difference is that the source becomes an image. 30 00:02:13,790 --> 00:02:19,760 The image uses forces dubbed JPG into the command below content detection dog type. 31 00:02:22,230 --> 00:02:22,560 That's. 32 00:02:22,590 --> 00:02:23,850 That's why it's your office. 33 00:02:23,850 --> 00:02:24,420 Seven. 34 00:02:26,350 --> 00:02:29,470 That's that source that the images horses not taped. 35 00:02:32,500 --> 00:02:32,890 The net. 36 00:02:32,890 --> 00:02:33,360 Enter. 37 00:02:34,570 --> 00:02:36,910 The detection results will then look like this. 38 00:02:40,000 --> 00:02:41,470 So exit press Q. 39 00:02:47,770 --> 00:02:50,050 Next detect objects on the webcam. 40 00:02:50,140 --> 00:02:53,020 If detection on webcam source becomes zero. 41 00:02:53,530 --> 00:02:54,970 Enter the command below. 42 00:02:55,630 --> 00:02:57,250 Fighting detection Dog point. 43 00:02:58,290 --> 00:02:58,620 That's. 44 00:02:58,620 --> 00:02:59,880 That's why it's your office. 45 00:02:59,880 --> 00:03:00,450 Seven. 46 00:03:01,460 --> 00:03:02,090 That's that. 47 00:03:02,090 --> 00:03:03,470 Source zero. 48 00:03:04,240 --> 00:03:04,660 Then it. 49 00:03:09,650 --> 00:03:11,990 The detection results will then look like this. 50 00:03:13,370 --> 00:03:16,850 In this example, there are four objects that have been successfully detected. 51 00:03:19,310 --> 00:03:20,780 So exit press Q. 52 00:03:26,540 --> 00:03:27,470 Congratulations. 53 00:03:27,500 --> 00:03:32,450 You have successfully run the first Deep Learning program which can detect 80 different object types 54 00:03:32,990 --> 00:03:33,830 in this video. 55 00:03:33,860 --> 00:03:36,130 Only use the CPU to detect objects. 56 00:03:36,140 --> 00:03:39,200 If you want to use the CPU, follow the next videos. 57 00:03:39,200 --> 00:03:40,040 See you then. 4198