If you're facing errors while converting an ONNX model to TensorFlow Lite, first simplify the model using onnxsim to remove unnecessary operations. Then, use onnx-tf to convert the ONNX model into a TensorFlow SavedModel. While converting the SavedModel to TFLite, if you get errors related to unsupported operations, enable allow_custom_ops=True. For quantization errors, try using dynamic range quantization with converter.optimizations = [tf.lite.Optimize.DEFAULT]. Make sure your model's input shape is static, as dynamic shapes often cause issues. Start by converting the model without quantization to ensure it works in float. Lastly, always use the latest version of the ONNX and Tensor Flow tools for better compatibility.https://gtamob.com/