1 00:00:00,990 --> 00:00:08,220 ‫Now, in this section, we are going to discuss the architecture of some popular CNN models, which 2 00:00:08,220 --> 00:00:11,110 ‫one image classification competitions in the past. 3 00:00:12,990 --> 00:00:14,610 ‫There are two reasons for doing this. 4 00:00:15,810 --> 00:00:23,160 ‫First is we want to understand these architectures because this will build our intuition as to what 5 00:00:23,430 --> 00:00:25,490 ‫is a good CNN model architecture. 6 00:00:27,390 --> 00:00:34,150 ‫And the second reason is that these train models can be used by us in our software. 7 00:00:34,410 --> 00:00:43,410 ‫Without retraining, these models with the architecture and the trained weights can be downloaded as part 8 00:00:43,410 --> 00:00:44,790 ‫of the keras Library only. 9 00:00:46,380 --> 00:00:53,880 ‫But how will these train models, which are trained on other set, how can these be used for our classification 10 00:00:53,880 --> 00:00:54,240 ‫problem? 11 00:00:55,800 --> 00:00:58,860 ‫We will see the answer to this question in the coming lectures. 12 00:00:59,910 --> 00:01:08,070 ‫Before we discuss these architectures, I briefly tell you about the image net competition which as 13 00:01:08,070 --> 00:01:10,440 ‫given us these popular architectures. 14 00:01:11,580 --> 00:01:15,030 ‫So imageNet competition, which is known as ILSVRC 15 00:01:15,160 --> 00:01:16,920 ‫for short. 16 00:01:17,930 --> 00:01:19,410 ‫Stand for image net 17 00:01:19,620 --> 00:01:22,680 ‫Large scale visual recognition challenge. 18 00:01:23,910 --> 00:01:27,360 ‫This challenge was held between 2010 and 2017. 19 00:01:27,660 --> 00:01:35,970 ‫Every year, participants in this challenge were given a data set of images and they had to classify 20 00:01:36,000 --> 00:01:37,920 ‫those images into several labels 21 00:01:40,040 --> 00:01:47,550 ‫In some of the competitions, the dataset had over a million observations and classes to be identified 22 00:01:47,570 --> 00:01:48,590 ‫were in thousands 23 00:01:49,460 --> 00:01:51,680 ‫So it was a very large scale competition. 24 00:01:53,930 --> 00:02:00,020 ‫The goal of this challenge was to promote the development of better computer vision techniques. 25 00:02:01,340 --> 00:02:03,860 ‫And also to benchmark the state of the art. 26 00:02:04,580 --> 00:02:12,610 ‫So the winner of each year, that convolutional network was the benchmark network. 27 00:02:12,860 --> 00:02:15,440 ‫It was considered as the best of its time. 28 00:02:18,910 --> 00:02:21,310 ‫So here are some popular CNN architectures. 29 00:02:21,940 --> 00:02:25,510 ‫Some of these also won the ILSVRC challenge. 30 00:02:27,760 --> 00:02:29,610 ‫The first one here is LeNet. 31 00:02:31,270 --> 00:02:34,990 ‫This was the oldest and most popular CNN architecture. 32 00:02:37,150 --> 00:02:39,080 ‫It has only 60000 parameters. 33 00:02:39,370 --> 00:02:42,890 ‫And if you look at it, it was made in nineteen ninety eight. 34 00:02:43,930 --> 00:02:49,840 ‫So even in 1998, convolutional neural networks were there and they were gaining popularity. 35 00:02:51,400 --> 00:03:00,370 ‫But a major breakthrough came in 2012 when Alex Net was able to achieve very high accuracy on previous 36 00:03:00,610 --> 00:03:02,030 ‫image classification problems. 37 00:03:04,380 --> 00:03:14,250 ‫This raise the interest of people in CNN and in 2013, ZF Net won the ILSVRC challenge 38 00:03:14,810 --> 00:03:16,900 ‫and it was a convolutional network. 39 00:03:18,900 --> 00:03:22,770 ‫It was able to achieve accuracy rate of nearly 85 percent. 40 00:03:24,600 --> 00:03:29,820 ‫In 2014, we got two very popular architectures. 41 00:03:30,180 --> 00:03:34,020 ‫One is GoogleNet and the other is VGGNet 42 00:03:35,880 --> 00:03:36,990 ‫GoogleNetwas the winner. 43 00:03:37,080 --> 00:03:37,500 ‫VGGNet 44 00:03:37,500 --> 00:03:41,720 ‫Net was runnerup. GoogleNet 45 00:03:41,730 --> 00:03:50,110 ‫had four million parameters as compared to VGGNet, which had 138 million parameters in 46 00:03:50,160 --> 00:03:50,730 ‫2005 47 00:03:50,820 --> 00:03:55,120 ‫The winner was ResNet and in 2016 and 17. 48 00:03:55,150 --> 00:03:57,730 ‫Also, there were two other architectures which won. 49 00:03:58,410 --> 00:04:02,040 ‫But these are the most popular architectures that you should know about. 50 00:04:03,510 --> 00:04:05,460 ‫We look at LeNet. 51 00:04:06,120 --> 00:04:09,660 ‫GoogleNet and VGGNet in more detail.