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These are the user uploaded subtitles that are being translated: 1 00:00:00,480 --> 00:00:07,650 Hello and welcome to this new tutorial now that we've done our four transformations the input is ready 2 00:00:07,830 --> 00:00:09,490 to be fed into the neural network. 3 00:00:09,510 --> 00:00:12,060 It has the authorization to get in. 4 00:00:12,180 --> 00:00:14,730 And therefore that's exactly what we're going to do. 5 00:00:14,730 --> 00:00:16,420 We're going to get it. 6 00:00:16,490 --> 00:00:25,380 And so there is nothing more simple you know we already have our pre-trained model SSD and it is already 7 00:00:25,380 --> 00:00:30,090 pre-trained because we could load the weight thanks to this file but that's not actually what we'll 8 00:00:30,090 --> 00:00:30,210 do. 9 00:00:30,210 --> 00:00:32,730 Now we will load the weight in the end. 10 00:00:32,730 --> 00:00:38,280 Right now that is just a variable so we will just use the variable but then at the end of this implementation 11 00:00:38,280 --> 00:00:41,970 we will load the weights to get our pre-trained model. 12 00:00:41,970 --> 00:00:50,460 So to feed x r towards variable that contains both the torch tensor of the input frame and the gradient 13 00:00:50,850 --> 00:00:52,890 into the neural network net. 14 00:00:53,160 --> 00:00:59,960 Well we simply need to take our neural network net and then apply X and that's it. 15 00:01:00,030 --> 00:01:02,940 That's how we feed X to the neural network. 16 00:01:03,150 --> 00:01:11,040 But then since this neural network nets applied to the input X will return the output y. 17 00:01:11,280 --> 00:01:19,740 Well we're going to get this output y right now and therefore and adding y equals net X. That gives 18 00:01:19,740 --> 00:01:26,190 us the output way we will of course describe what is why directly you can already start to try to think 19 00:01:26,280 --> 00:01:27,570 what it is exactly. 20 00:01:27,570 --> 00:01:30,140 But now we have the output that's great. 21 00:01:30,240 --> 00:01:32,970 And so we can move on to the next step. 22 00:01:33,060 --> 00:01:35,280 So the next step what is the next step. 23 00:01:35,460 --> 00:01:43,020 Well we just got our output y Why doesn't contain directly what we're interested in that is the result 24 00:01:43,020 --> 00:01:50,580 of the detection whether we have a dog or a human in the input frame to get that specific information 25 00:01:50,580 --> 00:01:51,830 we're interested in. 26 00:01:51,840 --> 00:01:55,460 Well we need to take the data attribute from Y. 27 00:01:55,560 --> 00:02:01,340 And so what we're going to do now is create a new sensor that we're going to call detections. 28 00:02:01,490 --> 00:02:06,110 So detection is a new tensor and that's a tensor contained in the output y. 29 00:02:06,210 --> 00:02:13,260 And that will contain the values we're interested in and to get this tensor while we take our output 30 00:02:13,260 --> 00:02:13,780 y. 31 00:02:14,010 --> 00:02:21,410 And then we add that and we take our attribute data and then we get the values of the output. 32 00:02:21,570 --> 00:02:22,050 Perfect. 33 00:02:22,050 --> 00:02:29,610 Now we have what we want the next step now is to create a new tensor object which will have the dimensions 34 00:02:29,730 --> 00:02:32,390 width height width height. 35 00:02:32,460 --> 00:02:34,190 So I didn't say twice. 36 00:02:34,200 --> 00:02:37,030 It's just a tensor of four dimensions. 37 00:02:37,080 --> 00:02:42,000 The first dimension is with the second dimension is height the third dimension is width and the fourth 38 00:02:42,000 --> 00:02:43,070 dimension is height. 39 00:02:43,290 --> 00:02:47,800 And now of course most of you must be thinking why do we have to create such a tensor. 40 00:02:47,940 --> 00:02:54,870 Well that's because the position of the detected objects inside the image has to be normalized between 41 00:02:55,020 --> 00:02:56,410 0 and 1. 42 00:02:56,460 --> 00:03:01,980 And to do this normalization will need this scale tensor with these four dimensions. 43 00:03:02,130 --> 00:03:04,910 Basically the Newtons are we're about to create right now. 44 00:03:04,950 --> 00:03:10,950 Scale will be just use to do this normalization between zero and one of the positions of the object 45 00:03:11,100 --> 00:03:12,320 detected in the image. 46 00:03:12,330 --> 00:03:13,820 That's the only purpose. 47 00:03:13,940 --> 00:03:16,810 And now why do we have with height width height. 48 00:03:16,840 --> 00:03:22,920 That's because the first two with height will correspond to the scale of values of the upper left corner 49 00:03:23,160 --> 00:03:24,990 of the rectangle detector. 50 00:03:25,230 --> 00:03:31,140 And the second with height will correspond to the scale of values of the lower right corner of this 51 00:03:31,140 --> 00:03:32,630 same rectangle detector. 52 00:03:32,640 --> 00:03:34,500 That's why we have a double with height. 53 00:03:34,620 --> 00:03:40,410 So let's create this scale sensor so that you can visualize it. 54 00:03:40,410 --> 00:03:48,330 So on a general rule to create a tensor in Torch Well we need to take our torche library and then we 55 00:03:48,330 --> 00:03:54,810 use the tensor class so scale will be an object of the tenso class which therefore will be a tensor 56 00:03:54,890 --> 00:03:56,150 a torch tensor. 57 00:03:56,550 --> 00:04:02,940 But as the arguments of this tensor class we need to specify the four dimensions of the tensor and these 58 00:04:02,940 --> 00:04:11,850 four dimensions are it's hights what's heights. 59 00:04:12,080 --> 00:04:12,790 Perfect. 60 00:04:12,800 --> 00:04:20,090 So this first with hied correspond to the upper left corner of the rectangle and this second with height 61 00:04:20,120 --> 00:04:23,220 corresponds to the lower right corner of the rectangle. 62 00:04:23,300 --> 00:04:29,060 And we're doing this to normalize the scale of values of the position of the detected objects between 63 00:04:29,150 --> 00:04:30,160 0 and 1. 64 00:04:30,260 --> 00:04:31,190 Perfect. 65 00:04:31,190 --> 00:04:32,610 So another good thing done. 66 00:04:32,690 --> 00:04:34,290 Don't worry about the warnings here. 67 00:04:34,310 --> 00:04:40,440 That's just because we haven't use these detections and scale variables yet we will do it very quickly. 68 00:04:40,640 --> 00:04:45,770 But before we do that I highly recommend to take a break because what we're about to do now will be 69 00:04:45,770 --> 00:04:49,700 slightly more complicated than what we've been doing so far. 70 00:04:49,730 --> 00:04:52,570 So we're going to finish with this tutorial now. 71 00:04:52,640 --> 00:04:54,080 Take a good break. 72 00:04:54,170 --> 00:04:59,100 Possibly a little nap or good coffee and then we'll attack more. 73 00:04:59,120 --> 00:05:01,670 The heart of the ass is tomorrow. 74 00:05:01,820 --> 00:05:03,390 Hope that didn't sound too aggressive. 75 00:05:03,480 --> 00:05:07,430 But yeah we're going to get into the heart of the as is the neural network. 76 00:05:07,520 --> 00:05:10,660 So have a good break and I'll see you in the next tutorial. 77 00:05:10,670 --> 00:05:12,380 Until then enjoy computer vision. 7822