What is the difference between inception v2 and Inception-v3?

What is the difference between inception v2 and Inception-v3?

In the Inception V2 architecture. The 5×5 convolution is replaced by the two 3×3 convolutions. As we discussed above that a 3×3 convolution can be converted into 1×3 then followed by 3×1 convolution which is 33% cheaper in terms of computational complexity as compared to 3×3.

Why is inception v3 good?

Inception v3 is a widely-used image recognition model that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. The model is the culmination of many ideas developed by multiple researchers over the years.

What’s the difference between inception V2 and V3?

In the Inception V2 architecture. The 5×5 convolution is replaced by the two 3×3 convolutions. This also decreases computational time and thus increase computational speed because a 5×5 convolution is 2.78 more expensive than 3×3 convolution. So, Using two 3×3 layers instead of 5×5 increases the performance of architecture.

What’s the difference between inception-ResNet V1 and V2?

Inception-ResNet v1 has a computational cost that is similar to that of Inception v3. Inception-ResNet v2 has a computational cost that is similar to that of Inception v4. They have different stems, as illustrated in the Inception v4 section. Both sub-versions have the same structure for the modules A, B,…

Which is the lowest error in inception V1?

It has produced the record lowest error at ImageNet classification dataset but there are some points on which improvement can be made to improve the accuracy and decrease the complexity of the model. Inception V1 have sometimes use convolutions such as 5*5 that causes the input dimensions to decrease by a large margin.

Why did inception V2 reduce representational bottleneck?

Inception v2 explores the following: Reduce representational bottleneck. The intuition was that, neural networks perform better when convolutions didn’t alter the dimensions of the input drastically. Reducing the dimensions too much may cause loss of information, known as a “representational bottleneck”