Image Classification in Computer Vision Applications Using Practical Tips for Transfer Learning

Transfer learning is a powerful technique in deep learning that can apply already learned knowledge to different but related tasks. This technique is particularly useful in computer vision, where collecting and annotating large amounts of image data is very expensive. This article explores practical techniques for using transfer learning in the field of image classification.

The first consideration is the data set. When using transfer learning, a large and diverse training data set is required. To save time and cost, you can choose to use public and open source data sets.

The first step in deep transfer learning (DTL) is to establish a good baseline model. The establishment of a baseline model can be achieved by selecting appropriate image size, backbone network, batch size, learning rate and number of epochs. These choices determine the performance and training effectiveness of the model. Through rapid iteration and experimentation, the baseline model can help us conduct subsequent deep transfer learning research and experiments.

After establishing a good baseline model, the next step is to fine-tune the learning rate and epoch number. This step is very important in deep transfer learning because it has a significant impact on the performance of the model. When choosing the learning rate and epoch number, it needs to be determined based on the characteristics of the backbone network and data set. For learning rates, a good starting range is usually between 0.0001 and 0.001. If the learning rate is set too high, the model may fail to converge; if the learning rate is set too low, the model may converge too slowly. Therefore, through experiments and observation of the training situation of the model, the learning rate is gradually adjusted to achieve the best performance. For epoch number, a good starting range is usually between 2 and 10. The number of epochs refers to the number of times that all samples in the training set are used completely once. A smaller number of epochs may lead to underfitting of the model.

After adjusting the learning rate and number of rounds, you can consider expanding the training images to improve model performance. Commonly used enhancement methods include horizontal and vertical flipping, resizing, rotating, moving, shearing, and techniques such as Cutmix and Mixup. These augmentation methods are able to randomly change the training images, making the model more robust.

The next step is to optimize the complexity of the model and input. This can be achieved by adjusting the complexity of the model or adjusting the backbone. This step aims to find the best model for the specific task and data.

After adjusting the model and input complexity, you can further optimize the model by increasing the image size, trying different backbones or architectures.

The last step is to retrain the model on the complete training data and perform model blending. This step is very critical because the more data used to train the model, the better its performance will be. Model blending is a technique that combines multiple models to improve overall model performance. When doing model blending, it is important to use the same settings with different adjustments, such as using different backbone networks, data augmentation methods, training cycles, image sizes, etc. This can increase the diversity of the model and improve its generalization ability.

In addition to these steps, there are some tips you can use to improve model performance. One of them is Test Time Augmentation (TTA), which improves model performance by applying augmentation techniques to test data. Additionally, another approach is to increase the image size during inference, which helps improve model performance. Finally, the use of post-processing and 2nd stage models is also an effective means to improve model performance.

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