# Troubleshooting

Not all models can be converted for the desired RVC Platform. We outline here some common issues that might arise during the
conversion process.

## Common Issues

### Incorrect Conversion Parameters

Typically, mismatches occur in input/output tensor shapes, names, or types.

SOLUTION: Thoroughly check your model and the meaning of all conversion parameters and adjust them accordingly. You can help
yourself with the [Netron](https://netron.app/) tool to study the characteristics of an ONNX model.

### Incompatible Model Formats

The source model format might not be supported by the conversion platform or the target format might not be compatible with
certain features of the model.

SOLUTION: It is advised to first convert the source model to ONNX format as it opens up the most options at conversion for the
appropriate RVC Platform.

### Unsupported Operations

The source model might contain operations that the conversion tool or the target model format do not support.

SOLUTION: Consult the [supported operations](https://docs.luxonis.com/software-v3/ai-inference/conversion/supported-ops.md) for
the target platform you aim to run the conversion for. If the operation is not supported, consider replacing it with a supported
alternative. If no alternative is available, consider breaking down the model into simpler components that can be converted
separately and offload the problematic parts to on-host processing. You can help yourself with the
[onnx-modifier](https://github.com/ZhangGe6/onnx-modifier) tool to introduce modifications to an ONNX model.

### Model Architecture Issues

Complex or non-standard model architectures might cause conversion issues.

SOLUTION: Simplify the model architecture if possible. Consider using the [onnx.checker](https://onnx.ai/onnx/api/checker.html)
tool to identify potential issues in an ONNX model.