All language subtitles for 010 Google Colab Face Mask Detection

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:03,070 --> 00:00:03,400 Okay. 2 00:00:03,400 --> 00:00:09,790 Then in the previous video, we have trained up to 63 epochs and got accuracy with P of 0.72. 3 00:00:09,820 --> 00:00:13,380 In this video we will try to detect face mask using the train weights. 4 00:00:13,390 --> 00:00:15,370 The notebook use is the same as before. 5 00:00:15,370 --> 00:00:16,840 The goal of seven notebook. 6 00:00:18,060 --> 00:00:21,990 If you haven't already mounted Google Drive, run the following three cells. 7 00:00:24,020 --> 00:00:27,110 If so, skip ahead to the face mask detection section. 8 00:00:31,030 --> 00:00:31,870 For detection. 9 00:00:31,870 --> 00:00:34,000 We have provided an image that can be downloaded. 10 00:00:34,030 --> 00:00:37,300 Therefore, the first code cell is used to download the image. 11 00:00:40,980 --> 00:00:43,770 Use the command g down, then link the image. 12 00:00:45,010 --> 00:00:47,410 When the sale by pressing the following button. 13 00:00:52,180 --> 00:00:54,430 The image is facemask doc PNC. 14 00:00:57,090 --> 00:00:59,550 Next move the image to the inference folder. 15 00:01:01,030 --> 00:01:04,870 Use the command mv face, mask, dock and Z inference. 16 00:01:06,050 --> 00:01:08,390 When the sale by pressing the following button. 17 00:01:11,060 --> 00:01:14,900 Next we will detect object on the face mask, dot P and G image. 18 00:01:14,930 --> 00:01:17,390 Use the following command to perform detection. 19 00:01:18,160 --> 00:01:20,170 Titan detect the pine. 20 00:01:22,130 --> 00:01:28,250 That's that's why it's we use what's best, not pity, which is the words that have the highest p value. 21 00:01:29,060 --> 00:01:31,580 In contrast, we use 0.5. 22 00:01:33,120 --> 00:01:35,730 In the image size, we use 640. 23 00:01:35,970 --> 00:01:39,150 In source read inference face Mass dot three and G. 24 00:01:40,060 --> 00:01:42,430 When the sale by pressing the following button. 25 00:01:48,010 --> 00:01:50,350 Wait until the detection process is finished. 26 00:01:53,790 --> 00:01:57,510 When finished, the detection results will be stored in the following folder. 27 00:02:00,830 --> 00:02:04,040 Next, we add a function to display the image in this code cell. 28 00:02:04,070 --> 00:02:07,160 This function is also used in the detection on image section. 29 00:02:08,810 --> 00:02:11,120 When the sale by pressing the following button. 30 00:02:13,920 --> 00:02:15,480 Continue to the next cell. 31 00:02:16,080 --> 00:02:19,380 This cell will call the cell image function to display the detection result. 32 00:02:20,920 --> 00:02:22,570 Blocking its path like this. 33 00:02:24,290 --> 00:02:26,030 Then copy by pressing control. 34 00:02:26,070 --> 00:02:26,510 See? 35 00:02:29,010 --> 00:02:30,240 And there were quotes. 36 00:02:31,160 --> 00:02:33,740 Place the image pad by pressing control fee. 37 00:02:36,880 --> 00:02:39,250 When the sale by pressing the following button. 38 00:02:47,690 --> 00:02:48,650 Here are the results. 39 00:02:48,650 --> 00:02:50,450 The mask is detected properly. 40 00:03:02,810 --> 00:03:05,560 Four masks are correctly detected in this image. 41 00:03:08,720 --> 00:03:12,200 You can save the training results and run detection on your own computer. 42 00:03:17,810 --> 00:03:19,040 To perform the detection. 43 00:03:19,040 --> 00:03:23,150 You just need to download the weights with the highest MLP memory based doped. 44 00:03:24,650 --> 00:03:25,790 Congratulations. 45 00:03:25,790 --> 00:03:27,860 You have finished the detection project. 46 00:03:28,400 --> 00:03:29,870 See you in the next video. 3846

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