Welcome to our guide on creating a powerful image classification model using Python! In this article, we will explore how to distinguish between different gaming controllers, specifically the Microsoft Xbox and Sony PlayStation controllers, using a deep learning technique known as Vision Transformer (ViT).

Understanding the Vision Transformer (ViT)

The Vision Transformer is akin to a sophisticated inspector, capable of breaking down an image into manageable segments. Imagine you have a large puzzle; instead of tackling it all at once, you divide it into smaller pieces. This allows you to examine each piece closely and understand its position within the overall picture.

How the Model Works

Here’s a simplified breakdown of how the model processes images:

• The input image is divided into smaller segments (sub-images) of equal size.
• Each segment is transformed into a one-dimensional vector through a linear insertion.
• To maintain spatial awareness, positional information is added to these vectors.
• These vectors, along with a special classification vector, are sent through transformer encoder blocks, which include:
• Layer Normalization (LN)
• Multi-head Self-Attention (MSA)
• Residual Connections
• Multi-Layer Perceptron (MLP)
• The final classification is carried out based solely on the classification vector, which encapsulates all the vital information about the image.

Data Preparation

The data fed into the model is retrieved from an image search API, allowing the download of approximately 150 images per class. The dataset is split into:

• 75% for training
• 15% for validation

To ensure accurate data collection, a random sampling of images is done to verify that the fetched images are representative of the searches (e.g., “Microsoft Xbox controller” and “Sony PlayStation controller”).

Training the Model

After labeling and mapping the data, the images are prepared in batches. These batches are then fed randomly into a pre-trained ViT model using the ImageNet-21k dataset. Training, validation, and optimization methods are executed in PyTorch, with Atom being used as the optimizer.

Outcome

After validating predictions against the image labels, the model achieves an accuracy of approximately 53% in distinguishing between a PlayStation controller and an Xbox controller.

Sample Images

Microsoft Xbox Controller

Sony PlayStation Controller

Troubleshooting Tips

If you encounter issues during your implementation, here are a few troubleshooting tips:

• Ensure that your image URLs are valid and accessible to the API.
• Check that the data split correctly adheres to the expected ratios for training and validation.
• Validate that your model’s parameters are set appropriately for the optimizer being used.
• If accuracy is lower than expected, consider augmenting your dataset or refining the model’s architecture.

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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.