In the world of machine learning, fine-tuning pre-trained models can help achieve better performance on specific tasks, such as image classification. In this blog, we will guide you through the process of fine-tuning the ViT (Vision Transformer) model, which has already undergone fine-tuning on the EuroSAT dataset.
Model Overview
The vit-base-patch16-224-finetuned-eurosat model is built on the ViT architecture. This model has been trained to classify images from the EuroSAT dataset, achieving a noteworthy accuracy of approximately 0.4865. Let’s explore the critical components of this setup.
Training Hyperparameters
The following hyperparameters were employed during the training process:
- Learning Rate: 5e-05
- Train Batch Size: 16
- Eval Batch Size: 16
- Seed: 42
- Gradient Accumulation Steps: 4
- Total Train Batch Size: 64
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Learning Rate Scheduler Type: Linear
- Learning Rate Scheduler Warmup Ratio: 0.1
- Number of Epochs: 1
Training Results
The model displayed the following training results after fine-tuning:
- Training Loss: 1.6128
- Validation Loss: 1.3905
- Accuracy: 0.4865
Understanding the Training Process with an Analogy
Consider a fitness coach who knows how to train athletes but is given a new group of people who want to run a marathon. Initially, the coach assesses the running ability of each participant (model evaluation). Based on this assessment, the coach then adjusts training plans (hyperparameters) to meet specific needs while focusing on safe progression. Over time, through consistent training and evaluation, the participants show improvement and enhance their stamina—a parallel to the fine-tuning process of the ViT model. The coach (the training process) uses what he knows to adapt to his team’s needs, helping them gradually reach their goals.
Troubleshooting Common Issues
As you embark on your fine-tuning journey, you might encounter some issues. Here are a few troubleshooting ideas:
- If the accuracy does not meet expectations, consider adjusting your learning rate or batch size.
- In case of overfitting, try implementing data augmentation or regularization methods.
- If you experience memory issues, reduce the batch size or utilize gradient accumulation.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Additional Resources
It’s important to refer to the following frameworks that have been utilized:
- Transformers: 4.25.1
- Pytorch: 1.13.0+cu116
- Datasets: 2.7.1
- Tokenizers: 0.13.2
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.