In the world of machine learning, image classification is a powerful technique that allows us to categorize images into predefined groups. In this guide, we will explore how to fine-tune the vit-base model specifically for classifying images of beans, leveraging the googlevit-base-patch16-224-in21k model from Hugging Face.
Understanding the vit-base Beans Model
The vit-base-beans-demo model is fine-tuned on a dataset containing various images of beans. It excels in classifying bean images with an impressive accuracy score of approximately 97.74%. Think of this model as a sophisticated chef who has specialized in identifying different types of beans; over time, with practice and experience, it has learned to recognize subtle differences among them.
Model Evaluation Metrics
- Loss: 0.0853
- Accuracy: 0.9774
These metrics indicate how well the model performed on the evaluation set. A lower loss value and a high accuracy percentage are good signs of a well-trained model.
Training Procedure
Here’s a breakdown of the training procedure used, incorporating various hyperparameters that are crucial for fine-tuning:
- Learning Rate: 0.0002
- Training Batch Size: 16
- Evaluation Batch Size: 8
- Seed: 42
- Optimizer: Adam (betas=(0.9,0.999), epsilon=1e-08)
- Learning Rate Scheduler: Linear
- Number of Epochs: 5
- Mixed Precision Training: Native AMP
Training Results
The following table outlines the training results at various epochs:
| Epoch | Step | Validation Loss | Accuracy |
|-------|------|-----------------|----------|
| 1 | 100 | 0.1436 | 0.9624 |
| 4 | 200 | 0.1058 | 0.9699 |
| 5 | 300 | 0.0853 | 0.9774 |
Troubleshooting
If you encounter issues while working with the vit-base-beans-demo model, consider the following troubleshooting ideas:
- Ensure your dataset is properly formatted and contains sufficient images for training.
- Adjust hyperparameters like learning rate and batch size; they can significantly impact model performance.
- If the model’s accuracy isn’t improving, consider increasing the number of epochs.
- Always verify that your environment has the correct versions of essential libraries like Transformers and Pytorch.
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Conclusion
By following this guide, you’ll be able to fine-tune the vit-base-beans-demo model effectively for your image classification tasks. 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.
