CNN Architecture  Every Machine Learning Engineer Should Know

10

1. LeNet-5

LeNet-5 is a pioneering CNN for recognizing handwritten digits, laying the groundwork for modern deep learning architectures.

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AlexNet revolutionized image classification with its deep architecture, winning the ImageNet challenge and popularizing CNNs.

2. AlexNet

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VGGNe is characterized by its simplicity, featuring uniform architecture with small convolutional filters for various computer vision tasks.

3. VGGNet

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ResNet introduces residual connections to tackle the vanishing gradient problem, enabling training of exceptionally deep neural networks.

4. ResNet

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DenseNe fosters feature reuse by connecting each layer to every other layer in a feed-forward manner, leading to compact yet powerful architectures.

5. DenseNet

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GoogLeNet incorporates inception modules for efficient feature extraction at different scales, enhancing performance while maintaining computational efficiency.

6. GoogLeNet (Inception)

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MobileNet is optimized for mobile and embedded vision applications, leveraging depthwise separable convolutions for lightweight yet accurate models.

7. MobileNet

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EfficientNet achieves state-of-the-art performance by scaling network depth, width, and resolution simultaneously using a compound scaling method.

8. EfficientNet

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9. YOLO (You Only Look Once)

YOLO is a real-time object detection system that directly predicts bounding boxes and class probabilities from images in a single pass.

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Combining CNNs with transformer architectures, exemplified by Vision Transformer (ViT), these models utilize self-attention mechanisms for image classification, achieving competitive results.

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10. Transformer-based CNNs

Do you want to  Learn more about CNN Architecture?

Take a look at Interviewbit's blog to gain an understanding of CNN Architecture, its various layers, and the different types of CNN Architecture.

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