In the rapidly evolving world of artificial intelligence, fine-tuning pre-trained models is a common practice that enhances their performance on specific tasks. Here, we will explore how to fine-tune the Swin Tiny model for image classification using an image folder dataset, and achieve impressive accuracy rates.

Understanding the Swin Tiny Model

The Swin Tiny model, specifically the swin-tiny-patch4-window7-224 architecture, is designed for image classification tasks. Think of it like a well-trained chef who not only knows the basic recipes but can also adapt those recipes based on the ingredients available and the desired outcome. By fine-tuning this model, we teach it how to better recognize the unique features of the images in our specific dataset.

Model Performance

This model has shown remarkable performance with an accuracy of about 98.26% on the evaluation set after fine-tuning. Here are the key metrics:

• Loss: 0.0552
• Accuracy: 0.9826

Hyperparameters Used for Training

Fine-tuning involves adjusting several hyperparameters. Here is a summary of the settings employed in our training:

• Learning Rate: 5e-05
• Train Batch Size: 32
• Eval Batch Size: 32
• Seed: 42
• Gradient Accumulation Steps: 4
• Total Train Batch Size: 128
• Optimizer: Adam (with betas=(0.9,0.999) and epsilon=1e-08)
• Learning Rate Scheduler: Linear
• Warmup Ratio: 0.1
• Number of Epochs: 3

Training Results Overview

The training results provide insight into how the model improved over the epochs. For instance:

Epoch: 1, Validation Loss: 0.0954, Accuracy: 0.9730
Epoch: 2, Validation Loss: 0.0614, Accuracy: 0.9822
Epoch: 3, Validation Loss: 0.0552, Accuracy: 0.9826

This is akin to tuning a musical instrument. At first, the sound may be off, but with each adjustment, the instrument (our model) learns to produce sweet melodies (accurate predictions).

Troubleshooting Tips

If you encounter issues while training or fine-tuning the model, consider the following troubleshooting steps:

• Check Data Quality: Ensure your images are correctly labeled and formatted. Poor quality data can lead to misleading accuracy results.
• Adjust Hyperparameters: If the accuracy is not improving, try tweaking learning rates, batch sizes, or the optimizer settings.
• Monitor Overfitting: Keep an eye on the training and validation loss to avoid overfitting, where the model performs well on training data but poorly on validation sets.
• Hardware Limitations: Ensure you have the necessary computational resources, such as a compatible GPU, to prevent training bottlenecks.

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Conclusion

Fine-tuning the Swin Tiny model can significantly enhance its ability to classify images accurately. By carefully selecting hyperparameters and monitoring training performance, you can achieve results that can be used in various practical applications. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.