Image Classification With Mobilenet-v3 Transfer Learning
Transfer learning is a technique in machine learning where a model trained on a large dataset is fine-tuned for a specific task. Mobilenet-v3 is a lightweight deep learning model that has been optimized for mobile and edge devices.
Image classification is a common task in computer vision where a model is trained to classify images into different categories. In this article, we will explore how to use Mobilenet-v3 for image classification using transfer learning.
Step 1: Preparing the Data
The first step in image classification is to prepare the dataset. This can be done by collecting a set of images for each category that you want to classify. It is important to have a balanced dataset with equal number of images for each category.
Step 2: Fine-tuning Mobilenet-v3
Once the dataset is prepared, we can fine-tune the Mobilenet-v3 model for image classification. This involves loading the pre-trained Mobilenet-v3 model and replacing the top layer with a new output layer that has the number of classes equal to the number of categories in the dataset.
Step 3: Training the Model
After fine-tuning the model, we can train it on the dataset using transfer learning. During training, the weights of the pre-trained Mobilenet-v3 model are frozen while the weights of the new output layer are updated to classify the images correctly.
Step 4: Evaluating the Model
Once the model is trained, we can evaluate its performance on a separate test dataset. We can measure the accuracy, precision, recall, and F1 score of the model to assess its performance.
Conclusion
Image classification with Mobilenet-v3 transfer learning is a powerful technique in computer vision that allows us to classify images with high accuracy using a lightweight model. By following the steps outlined in this article, you can easily build an image classification model using Mobilenet-v3 and transfer learning.
