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All language subtitles for 004 Windows How to continue an unfinished training

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,060 --> 00:00:04,510 Okay, then in the previous video training was talked before the specified epochs. 2 00:00:04,530 --> 00:00:07,620 This video will explain how to continue the previous training. 3 00:00:07,710 --> 00:00:11,160 Training will continue with the last weights to continue training. 4 00:00:11,190 --> 00:00:18,600 Type the following command content framework P in workers set zero in the back side we write for. 5 00:00:22,160 --> 00:00:23,480 On device zero. 6 00:00:25,350 --> 00:00:26,010 In the data. 7 00:00:26,040 --> 00:00:26,250 Right. 8 00:00:26,280 --> 00:00:28,320 The data file that was previously created. 9 00:00:33,210 --> 00:00:35,490 But IMDB we write 640. 10 00:00:38,220 --> 00:00:42,030 In the CFG write the configuration file that was previously created. 11 00:00:54,730 --> 00:00:55,300 In words. 12 00:00:55,330 --> 00:00:57,790 We use the last words from the previous training. 13 00:00:58,270 --> 00:01:00,090 In this example, it is in response. 14 00:01:01,820 --> 00:01:02,450 Train. 15 00:01:05,209 --> 00:01:06,950 YOLO v seven Face masks. 16 00:01:09,210 --> 00:01:09,900 Wait. 17 00:01:12,270 --> 00:01:13,500 Last the pit. 18 00:01:20,240 --> 00:01:21,560 In the name we write YOLO. 19 00:01:21,560 --> 00:01:22,970 Five seven Face mask. 20 00:01:29,180 --> 00:01:32,420 In head use hip scratch custom file in the data folder. 21 00:01:34,370 --> 00:01:35,060 In epochs. 22 00:01:35,060 --> 00:01:36,140 We like 300. 23 00:01:42,500 --> 00:01:44,300 Finally at the resumed argument. 24 00:01:46,160 --> 00:01:47,900 Press enter to continue training. 25 00:01:55,950 --> 00:01:57,090 Here's an example. 26 00:01:57,150 --> 00:02:01,710 Training does not begin in a Fox zero, but rather at the last epoch of the previous training. 27 00:02:17,750 --> 00:02:23,030 We will stop at the 34th epoch because we have got a fairly large MLP of 0.77. 28 00:02:23,210 --> 00:02:29,120 We will stop at the 34th epoch because we have got a sufficient accuracy with MLP of 0.77. 29 00:02:36,170 --> 00:02:39,890 In the next video, we will try to detect face masks using the train weights. 30 00:02:39,920 --> 00:02:40,740 See you then. 2330