Welcome to your step-by-step guide on fine-tuning the ResNet18 model for image classification, specifically on a beans dataset! This article will equip you with the knowledge to navigate through the process smoothly. Let’s dive in!

Understanding the Model

The model we’re working with is a tailored version of the ResNet18 architecture, which is well-known for its deep learning prowess in image classification. By fine-tuning this model on the beans dataset, it learns to distinguish between various types of beans effectively.

What You Will Need

• Python environment with PyTorch installed
• Essential libraries: Transformers, Datasets, and Tokenizers
• The beans dataset

Model Results

After training, the model yielded the following results on the evaluation set:

• Loss: 1.3225
• Accuracy: 0.5789

Training Procedure

To understand how to efficiently train this model, let’s explore the configurations and steps involved:

Hyperparameters

Here are the key hyperparameters used during training:

• Learning Rate: 0.001
• Train Batch Size: 4
• Eval Batch Size: 8
• Seed: 42
• Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• Learning Rate Scheduler Type: Linear
• Training Steps: 10

Training Results

The training results after a few epochs are documented in the table below:

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---------------|-------|------|------------------|----------|
| 0.2601        | 0.02  | 5    | 2.8349           | 0.5113   |
| 1.8184        | 0.04  | 10   | 1.3225           | 0.5789   |

How to Fine-Tune

Now, let’s decode the process of fine-tuning the ResNet18 model as if we’re cooking a meal. Imagine that the ResNet18 is like a large pot, and you’re selecting ingredients (data) to create a delightful dish (a trained model).

• **Ingredients Selection**: Gather your beans dataset, similar to picking fresh ingredients from the market. This ensures your model has high-quality data for training.
• **Heat Settings (Hyperparameters)**: Set the heat (hyperparameters) to the right temperature – too high could scorch the beans (overfitting), too low could leave them raw (underfitting). For instance, adjusting the learning rate ensures that the model learns at the correct pace.
• **Cooking Time (Epochs)**: Cook the dish for just the right amount of time. Training for 10 epochs is akin to simmering your dish until the flavors are well blended – testing is key to knowing when it’s done!
• **Taste Testing (Validation)**: Don’t forget to taste your dish along the way. Monitoring accuracy and loss during validation gives insights into whether you need to adjust ingredients or cooking time.

Troubleshooting

If you encounter issues during your training process, consider the following troubleshooting tips:

• **Low Accuracy**: Check if your dataset is large enough and well-balanced. You might need more diverse samples or augmentations.
• **Overfitting**: If the model performs well on training data but poorly on validation data, consider reducing the complexity of your model or increasing dropout rates.
• **Noisy Data**: Sometimes, noisy or mislabeled data can confuse the model. Ensure that your beans dataset is clean and well-annotated.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

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.

We hope this guide has helped demystify the fine-tuning process of the ResNet18 model for image classification. Happy coding!