Comments (2)
That's great, that will help a lot.
It could help to also add some very simple models somewhere to help with getting started.
from onnx.
I think that we already have some of the standard models serialized in https://github.com/onnx/models, containing both the model and input
/output
files with the expected output of the models, which allows for an easy evaluation from the backend side.
from onnx.
Related Issues (20)
- SegFault during shape inference if the schema not be set inference function
- Generalize input/output checks from #5990 HOT 1
- OpenCV dnn cv.dnn.readNetFromONNX HOT 1
- Source build failed with cmake error HOT 5
- `clip` operation handles NaN values differently between CPU and GPU
- `clip` behaviour with NaN values is different between GPU and CPU onnx inference HOT 5
- There is an error in converting LLaVA model. It should not be adapted to this model. How should this model be converted to onnx HOT 1
- how onnx models stores weights and layer parameters
- [Feature request] checking an input rank is within a specific range HOT 6
- [Feature request] Shape Inference for Einsum instead of Rank Inference HOT 1
- Building failed with Vitis AI. HOT 1
- AttributeError while installing ONNX HOT 11
- Replace const string references with string_views HOT 1
- [ONNX] Onnx file loads very slowly with onnxruntime, which is exported (dynamic_shapes=True) with `torch.onnx_dynamo_export`. HOT 2
- Optimizing Node Ordering in ONNX Graphs: Ensuring Correct Sequence for Model Generation HOT 1
- When will outstanding security vulnerabilities be fixed? HOT 2
- ScatterND's doc gives wrong result for 3d indice case HOT 2
- Equivalent Implementations to ScatterElements operator HOT 3
- onnx.load() -> Can't Open this page HOT 1
- Square Activation is not supported by onnxruntime HOT 1
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from onnx.