Comments (1)
The converters for coremltools are not actively maintened. The unit test are not run anymore. Is there any way to convert the original model from pytorch or tensorflow to onnx instead?
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Related Issues (20)
- `TreeEnsembleRegressor` introduces an additional batch dimension HOT 2
- Mismatch between XGBoost and converted onnx, with respect to "colsample_bytree"
- XGBoost best_ntree_limit is removed in 2.0 and trying to access this value HOT 1
- XGBClassifier conversion to onnx gives wrong predictions if dataset has only 2 categories (xgboost 2.0)
- XGBRegressor conversion to onnx gives wrong predictions for xgboost 2.0
- No module named 'onnxconverter_common' HOT 2
- onnx converted : xgboostRegressor multioutput model predicts 1 dimension instead of original 210 dimensions. HOT 4
- UNABLE_TO_INFER_SCHEMA on pyspark
- Integrate with ONNX 1.16.0 release branch HOT 1
- Please help validate release candidate for ONNX 1.16.0rc2 HOT 1
- Python 3.9 CoreML conversion HOT 6
- RandomForestClassifier: ONNX output converts all BRANCH_LEQ to BRANCH_EQ HOT 1
- Standard HOT 1
- Adding support for SHAP explainability to tree model types HOT 3
- Converting LightGBM Regressor to ONNX seems impossible with mixed float and string initital_types. HOT 1
- Spark XGB converter accesses missing param improperly HOT 4
- ModuleNotFoundError: No module named 'pkg_resources' for Python 3.12+
- Updating `DEFAULT_OP_BLOCK_LIST` to different ONNX opsets
- Integrate with ONNX 1.17.0 release branch
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