In this guide, we’ll explore how to fine-tune a model using the Timm library for image classification. We will focus specifically on a model called my-cool-timm-model-3, which is built on the resnet18 architecture and trained on the famous Cats vs. Dogs dataset. Whether you’re a beginner or looking to refine your skills, this blog will provide a user-friendly walkthrough.

Understanding the Model

The my-cool-timm-model-3 is a fine-tuned variant of the resnet18 model. Think of it as a chef who has mastered a basic recipe (resnet18) but adds some unique flavors (fine-tuning) to make it even better for the specific task at hand—differentiating between images of cats and dogs. This approach enables the model to achieve impressive results, such as:

• Loss: 0.2455
• Accuracy (Top-1): 94.42%
• Accuracy (Top-5): 100%

Setting Up the Environment

Before diving into the training procedure, ensure you have the necessary libraries installed:

pip install timm torch torchvision

Training Procedure

Fine-tuning a model involves several hyperparameters that guide the training process. Here’s a breakdown of our configuration:

• Learning Rate: 5e-05
• Train Batch Size: 256
• Eval Batch Size: 256
• Seed: 42
• Optimizer: Adam (betas=(0.9, 0.999), epsilon=1e-08)
• Learning Rate Scheduler: Linear
• Training Steps: 10
• Mixed Precision Training: Native AMP

These hyperparameters help the model learn effectively and make the best predictions during evaluation.

Interpreting Training Results

After training, it’s vital to evaluate the model’s performance. The training results of our model are as follows:

| Training Loss | Epoch | Step | Validation Loss | Acc1     | Acc5   |
|---------------|-------|------|-----------------|----------|--------|
| 0.5152        | 0.14  | 10   | 0.2455          | 94.4175  | 100.0  |

Here, you can see how the model performed during the training epochs. The validation loss and accuracy metrics confirm that our model has learned effectively.

Troubleshooting Common Issues

If you encounter issues while fine-tuning your model, consider the following troubleshooting tips:

• Ensure all libraries are up-to-date and compatible.
• Check if the dataset is properly structured and accessible.
• Adjust the learning rate; sometimes, a smaller rate helps in better convergence.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.