In the world of image recognition, the DEIT_FLYSWOT model shines brightly, showcasing remarkable efficiency in classifying images. In this article, we will guide you through the setup, training procedure, and troubleshooting techniques of using this model effectively. Let’s get started!

Model Overview

The DEIT_FLYSWOT model has been trained from scratch on an image folder dataset. With a staggering F1 score of approximately 0.9908, it has demonstrated an excellent capability to classify images accurately. Here, we’ll break down the training procedure and hyperparameters that made this achievement possible.

Understanding the Training Procedure

To better understand the training process, imagine the DEIT_FLYSWOT model as a chef in a kitchen. The chef (model) gathers ingredients (data) and follows a recipe (training procedure) to create a delicious dish (predictions). The ingredients’ quality and the recipe’s accuracy determine how well the dish turns out.

Training Hyperparameters

Here are some essential parameters that acted as the recipe for our chef:

• Learning Rate: 2e-05
• Train Batch Size: 32
• Eval Batch Size: 32
• Seed: 666
• Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• LR Scheduler Type: Linear
• Number of Epochs: 30
• Mixed Precision Training: Native AMP

Training Results

The training results indicate how well our chef performed in the kitchen:

    Training Loss  Epoch  Step  Validation Loss  F1
    :-------------::-----::----::---------------::------:
    No log         1.0    52    0.5710           0.8095
    ...
    0.0755         30.0   1560  0.0755           0.9908

As you can see, with each epoch, our model improved its performance, like a chef refining their dish, ensuring every ingredient was just right.

Troubleshooting Tips

Even the best chefs face challenges in the kitchen. Here are some common issues you might encounter while using the DEIT_FLYSWOT model and how to solve them:

• Model Not Converging: If the model is not converging, consider reducing the learning rate or increasing the number of epochs.
• Overfitting: If your model performs well on training data but poorly on validation data, try using data augmentation or dropout techniques.
• Performance Fluctuations: Ensure the dataset is balanced and contains enough varied samples to avoid bias in predictions.

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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.