What’ s the format of the file to train a multi image recognition neural network?

The format of the file to train a multi-image recognition neural network can vary depending on the specific requirements and tools you are using. However, based on the context provided from the WWDC sessions, here are some key points:

1. Data Formats: Common data formats for training models include 32-bit and 16-bit floating point numbers. These formats are useful for updating parameters during training. After training, quantization techniques can be used to reduce memory requirements by representing values as 8-bit integers, which helps in reducing the model size and improving throughput (Train your machine learning and AI models on Apple GPUs).

2. Annotations and Data Sources: When building image-based models in the CreateML app, it is important to verify that your annotations match your expectations. You can explore your data sources and visualize annotations to ensure they are correct before starting the training (What’s new in Create ML).

3. 3D Models for Object Tracking: For object tracking, a 3D model representing the real-world object is required. This 3D asset needs to be in the USDZ file format to ensure the best results (Explore object tracking for visionOS).

4. Core ML Format: After training your model, you can convert it to the Core ML format using Core ML tools. This format is optimized for Apple hardware and supports various compression techniques (Explore machine learning on Apple platforms).

Relevant Sessions

• Train your machine learning and AI models on Apple GPUs
• What’s new in Create ML
• Explore object tracking for visionOS
• Explore machine learning on Apple platforms

These sessions provide detailed insights into the data formats, tools, and workflows for training multi-image recognition neural networks on Apple platforms.