MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset.
Here is an overview of the
model:
MobileNetV2 is a classification model (distinct from MobileNetSSDv2) developed by Google. It provides real-time classification capabilities under computing constraints in devices like smartphones. This implementation leverages transfer learning from ImageNet to your dataset.
The MobileNetV2 architecture utilizes an inverted residual structure where the input and output of the residual blocks are thin bottleneck layers. MobileNetV2 also uses lightweight convolutions to filter features in the expansion layer. Finally, it removes non-linearities in the narrow layers.
Image in Courtesy of Papers With Code
MobileNet V2 outperforms MobileNet V1 with higher accuracies and lower latencies.
Image in Courtesy of Google AI
How to Train MobileNetV2 On a Custom Dataset: https://blog.roboflow.com/how-to-train-mobilenetv2-on-a-custom-dataset/
MobileNetV2 Paper: https://arxiv.org/abs/1801.04381v4
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Roboflow offers a range of SDKs with which you can deploy your model to production.
MobileNet V2 Classification
uses the
uses the
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
You can automatically label a dataset using
MobileNet V2 Classification
with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use
MobileNet V2 Classification
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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