1 00:00:01,590 --> 00:00:07,590 ‫By now, you you'd have noticed that every architecture has in general two parts. 2 00:00:09,300 --> 00:00:13,140 ‫The first part of the architecture is a convolutional base. 3 00:00:14,610 --> 00:00:17,820 ‫This includes convolutional layers and pooling layers. 4 00:00:20,650 --> 00:00:24,310 ‫It could be one convolutional and one willingly. 5 00:00:24,460 --> 00:00:25,690 ‫It could be tens of them. 6 00:00:25,810 --> 00:00:26,960 ‫It could be hundreds of them. 7 00:00:28,120 --> 00:00:34,390 ‫However, if you look at the architectures, most of them in the first part had convolutional. 8 00:00:34,490 --> 00:00:41,800 ‫Basically, the output of that convolutional base goes into a fully connected neural network. 9 00:00:43,480 --> 00:00:46,720 ‫So the job of convolutional base is very generic. 10 00:00:47,430 --> 00:00:52,180 ‫It is to find out and highlight certain features from the input images. 11 00:00:53,860 --> 00:01:02,920 ‫For example, if you are inputting cats and dogs data, the job of convolutional base would be to highlight 12 00:01:03,070 --> 00:01:04,440 ‫eyes, ears. 13 00:01:04,760 --> 00:01:07,670 ‫This goes Glaus, etc.. 14 00:01:08,980 --> 00:01:16,180 ‫So all of these individual features of the image are highlighted by the conditional base. 15 00:01:17,890 --> 00:01:25,510 ‫The job of fully connected neural network is to use these identified features to classify the image, 16 00:01:25,630 --> 00:01:27,910 ‫whether it is a dog or whether it is a cat. 17 00:01:30,460 --> 00:01:40,090 ‫So if you have a neural network which is already trained in identifying certain features and then classifying 18 00:01:40,090 --> 00:01:47,920 ‫those images and now you have a new problem in which you are also trying to find the same features, 19 00:01:48,630 --> 00:01:51,310 ‫maybe you are trying to have a different classification. 20 00:01:51,580 --> 00:01:58,240 ‫But if the import images have the same features, in that case, you can use these same convolutional 21 00:01:58,240 --> 00:02:00,870 ‫base of retrain models. 22 00:02:03,040 --> 00:02:12,550 ‫For example, in 2014, the AI, unless we asked each challenge, had one million images of different 23 00:02:12,610 --> 00:02:19,000 ‫animals and there were 1000 different animals to which these images belong. 24 00:02:21,710 --> 00:02:30,920 ‫The convolutional base of the winning networks were identifying features of different animals and declassifies. 25 00:02:30,990 --> 00:02:36,720 ‫In the end, were only classifying those features and do which animal it is. 26 00:02:37,260 --> 00:02:38,880 ‫And what is the breed of that animal? 27 00:02:43,020 --> 00:02:51,850 ‫So now if we're only learning a model to classify cats and dogs, this is the similar kind of input 28 00:02:51,870 --> 00:02:54,600 ‫images that that particular talent had. 29 00:02:55,590 --> 00:03:03,300 ‫So a model that is trained on 2014, data that can be used in our problem also. 30 00:03:06,120 --> 00:03:11,070 ‫So this is the concept of crosswell learning or feature extraction. 31 00:03:11,940 --> 00:03:19,560 ‫We are going to use some part of our pre train model, mostly the convolutional base, because convolutional 32 00:03:19,560 --> 00:03:21,270 ‫base is more genetic. 33 00:03:21,690 --> 00:03:29,220 ‫It is only finding features and we will put a new classifier in front of the convolutional base. 34 00:03:29,790 --> 00:03:38,790 ‫That classifier will be trained by our system and that will be trained to classify and identify are 35 00:03:39,540 --> 00:03:42,390 ‫images into the classes that we have. 36 00:03:44,690 --> 00:03:46,820 ‫So this convolutional base will remain the same. 37 00:03:47,330 --> 00:03:51,620 ‫We will have a new classified on top of it to classify our images. 38 00:03:53,150 --> 00:04:00,950 ‫The advantages of doing this is that it saves a lot of time because we do not have to train this part 39 00:04:00,950 --> 00:04:01,700 ‫of the network. 40 00:04:03,230 --> 00:04:06,320 ‫Another good thing is these are proven models. 41 00:04:06,800 --> 00:04:09,710 ‫They are one of the best in finding the features. 42 00:04:10,580 --> 00:04:18,320 ‫So when we take their convolutional base, we can be assured that the features extracted from the images 43 00:04:18,740 --> 00:04:19,610 ‫would be the best. 44 00:04:21,670 --> 00:04:25,330 ‫Also, these models are trained on huge does it. 45 00:04:26,140 --> 00:04:29,740 ‫They had input data of millions of images. 46 00:04:31,000 --> 00:04:37,120 ‫So even if you have a small dataset from which featured extraction could have been difficult. 47 00:04:38,200 --> 00:04:44,710 ‫These models are already trained to extract features on large amount of data. 48 00:04:46,540 --> 00:04:49,030 ‫And the best thing is they're very easy to use. 49 00:04:49,570 --> 00:04:51,690 ‫They are part of the get us library. 50 00:04:52,240 --> 00:04:59,350 ‫It only takes a few lines of good to download all the weight of all the neurons in the convolutional 51 00:04:59,350 --> 00:04:59,710 ‫base. 52 00:04:59,950 --> 00:05:01,720 ‫And those can be used straightaway. 53 00:05:02,890 --> 00:05:11,110 ‫So in that project, we will see how using pre train models, we can achieve higher level of accuracy 54 00:05:11,320 --> 00:05:14,770 ‫even if we have a small amount of data to train model.