MobileNet v1-v3
MobileNet, a series of TensorFlow-based computer vision models, has evolved through various versions, each offering its unique specializations and improvements. Here are the key distinctions among them:
MobileNet V1: The first version of MobileNet, introduced in 2017, pioneered the use of depth-wise separable convolutions to build lightweight deep neural networks. It was known for its speed, being 10x faster and smaller than its predecessor, VGG16.
MobileNet V2: This version was built on the foundation of V1 but introduced significant improvements. The primary advancement was the addition of inverted residuals and linear bottlenecks, critically enhancing the model's performance without increasing its size. This allowed the model to retain its lightweight characteristic while improving accuracy.
Details about inverted residuals and linear bottlenecks
MobileNet V3: The concepts used in MobileNet V3 are an amalgamation of the innovations from the previous versions and novel technology advancements like hardware-aware network architecture search (NAS). This version significantly improves the model’s performance in both speed and accuracy.