Welcome to our exploration of the violation-classification-bantai_vit, a potent model fine-tuned on the googlevit-base-patch16-224-in21k architecture. This guide will discuss how to effectively utilize this image classification model, troubleshoot common issues, and dive deeper into its capabilities.
Understanding the Violation Classification Model
This model has been specifically trained to classify violations in images. It’s akin to teaching a dog to recognize different commands—it requires the right training data and careful hyperparameter tuning. After much diligence, the model notched an impressive evaluation accuracy of 94.78%, demonstrating its capability in differentiating between various image patterns related to violations.
Key Features of the Model
- Evaluation Loss: 0.2362
- Evaluation Runtime: 43.25 seconds
- Samples Processed Per Second: 85.42
- Steps Taken: 10,005 in total
- Training Epochs: Achieved over 87 epochs
Training Procedure
The journey to build this robust model involved several key hyperparameters:
- Learning Rate: 5e-05
- Training Batch Size: 32
- Evaluation 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 with warm-up ratio of 0.1
- Number of Epochs: 500
Troubleshooting Common Issues
While using the violation classification model, you may encounter a few bumps on the road. Here are some troubleshooting steps to help you along the way:
- High Evaluation Loss: If you notice a high evaluation loss, consider adjusting the learning rate, reevaluating your training dataset, or increasing the number of epochs.
- Low Accuracy: Check your data for imbalances or inadequate training samples. Sometimes adding more labeled data can significantly improve model performance.
- Slow Processing: If you’re facing slow processing times, ensure that your hardware acceleration is properly configured, or try reducing the batch size to see if performance improves.
- If you run into persistent issues or require further support, feel free to connect with the community for more insights. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
With the right understanding and configurations, the violation classification model can be a powerful tool in your AI toolkit. Remember to monitor your model’s performance and be willing to iterate on your training techniques. 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.